Systems and methods for analyzing and optimizing dynamic tolerance curves

ABSTRACT

A method for analyzing power quality events in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one metering device in the electrical system to generate at least one dynamic tolerance curve. Each dynamic tolerance curve of the at least one dynamic tolerance curve characterizes a response characteristic of the electrical system at a respective metering point in the electrical system. The method also includes analyzing the at least one dynamic tolerance curve to identify special cases which require further evaluation(s)/clarification to be discernable and/or actionable. The at least one dynamic tolerance curve may be regenerated or updated, and/or new or additional dynamic tolerance curves may be generated, to provide the further clarification. One or more actions affecting at least one component in the electrical system may be performed in response to an analysis of the curve(s).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority to U.S. Provisional Application No. 62/694,791, filed on Jul. 6, 2018; U.S. Provisional Application No. 62/770,730, filed on Nov. 21, 2018; U.S. Provisional Application No. 62/770,732, filed on Nov. 21, 2018; U.S. Provisional Application No. 62/770,737, filed on Nov. 21, 2018; U.S. Provisional Application No. 62/770,741, filed on Nov. 21, 2018; and U.S. Provisional Application No. 62/785,424, filed on Dec. 27, 2018, which applications were filed under 35 U.S.C. § 119(e) and are incorporated by reference herein in their entirety.

FIELD

This disclosure relates generally to power quality issues, and more particularly, to systems and methods for analyzing and optimizing dynamic tolerance curves in accordance with embodiments of this disclosure that may be used to analyze power quality issues or events in an electrical system.

BACKGROUND

As is known, power quality issues are one of the most significant and costly impacts on electrical systems (also sometimes referred to as “electrical networks”). Poor power quality is estimated to cost the European economy up to €150 billion annually, according to the Leonardo Power Quality Initiative.¹ Additionally, the U.S. economy experiences losses ranging from $119 billion to $188 billion annually, according to research by the Electric Power Research Institute (EPRI).² Perhaps the most important statistic is the EPRI finding that 80 percent of power-quality disturbances are generated within a facility. One exemplary economic model summarizes the total cost associated with power quality events as follows: Total losses=production losses+restart losses+product/material losses+equipment losses+third-party costs+other miscellaneous costs³ ¹ https://adfpowertuning.com/en/about-us/news-stories/148-leonardo-energy-qpan-european-power-quality-surveyq-shows-g150bn-annually-in-cost-for-low-power-quality.html² https://blog.schneider-electric.com/power-management-metering-monitoring-power-quality/2015/10/16/why-poor-power-quality-costs-billions-annully-and-what-can-be-done-about-it³ The Cost of Poor Power Quality, Roman Targosz and David Chapman, October 2015, ECI Publication No. Cu0145

Other miscellaneous costs associated with power quality issues may include intangible losses such as a damaged reputation with customers and suppliers or more direct losses such as the devaluation of credit ratings and stock prices.

SUMMARY

Described herein are systems and methods related to analyzing and optimizing dynamic tolerance curves in accordance with embodiments of this disclosure that may be used to analyze power quality issues or events in an electrical system. The electrical system may be associated with at least one load, process, building, facility, watercraft, aircraft, or other type of structure, for example. In one aspect of this disclosure, a method for analyzing power quality events in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one metering device (e.g., an intelligent electronic device (IED)) in the electrical system to generate at least one dynamic tolerance curve. Each dynamic tolerance curve of the at least one dynamic tolerance curve characterizes a response characteristic of the electrical system at a respective metering point in the electrical system. The method also includes analyzing the at least one dynamic tolerance curve to identify special cases which require further evaluation(s)/clarification to be discernable and/or actionable by the system that consumes, analyzes, processes, and/or presents the data or a user. The method further includes regenerating the at least one dynamic tolerance curve, updating the at least one dynamic tolerance curve, and/or generating new or additional dynamic tolerance curves to provide the further clarification. In some embodiments, the method also includes automatically performing one or more actions affecting at least one component in the electrical system, and/or initiating another process in a manufacturing SCADA or changes to the process(es), in response to a load loss being outside of a range indicated in the regenerated and/or updated at least one dynamic tolerance curve, and/or the new or additionally generated dynamic tolerance curves. The at least one component may include, for example, one or more loads monitored by the at least one metering device.

In another aspect of this disclosure, a method for analyzing power quality events in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices (e.g., IEDs) in the electrical system to generate or update a plurality of dynamic tolerance curves. In some embodiments, each of the plurality of dynamic tolerance curves characterizes a response characteristic of the electrical system at a respective metering point of a plurality of metering points in the electrical system. The method also includes selectively aggregating power quality data from the plurality of dynamic tolerance curves to analyze power quality events in the electrical system.

In some embodiments, the above methods may be implemented using one or more of the plurality of metering devices. Additionally, in some embodiments the above methods may be implemented remote from the plurality of metering devices, for example, in a gateway, a cloud-based system, on-site software, a remote server, etc. (which may alternatively be referred to as a “head-end” system herein). In some embodiments, the plurality of metering devices may be coupled to measure electrical signals, receive electrical measurement data from the electrical signals at an input, and configured to generate at least one or more outputs. The outputs may be used to analyze power quality events in an electrical system. Examples of the plurality of metering devices may include a smart utility meter, a power quality meter, and/or another metering device (or devices). The plurality of metering devices may include breakers, relays, power quality correction devices, uninterruptible power supplies (UPSs), filters, and/or variable speed drives (VSDs), for example. Additionally, the plurality of metering devices may include at least one virtual meter in some embodiments.

In embodiments, the above methods are generally applicable to non-periodic power quality issues or events such as transients, short-duration rms variations (e.g., sags, swells, momentary interruptions, temporary interruptions, etc.), and some long-duration rms variations (e.g., that may be as long as about 1-5 minute(s)).

Examples of electrical measurement data that may be captured by the plurality of metering devices may include at least one of continuously measured voltage and current signals and their derived parameters and characteristics. Electrical parameters and events may be derived, for example, from analyzing energy-related signals (e.g., real power, reactive power, apparent power, harmonic distortion, phase imbalance, frequency, voltage/current transients, voltage sags, voltage swells, etc.). More particularly, the plurality of metering devices may evaluate a power quality event's magnitude, duration, load impact, recovery time from impact, unproductive recovery energy consumed, CO2 emissions from recovery energy, costs associated with the event, and so forth.

It is understood there are types of power quality events and there are certain characteristics of these types of power quality events, as described further below in connection with the table from IEEE Standard 1159-2009 (known art) provided below and the paragraph that immediately precedes the table, for example. A voltage sag is one example type of power quality event. The distinctive characteristics of voltage sag events are the magnitude of the voltage sag and its duration, for example. As used herein, examples of power quality events may include voltage events impacting phase, neutral, and/or ground conductors and/or paths.

The above method, and the other methods (and systems) described below, may include one or more of the following features either individually or in combination with other features in some embodiments. In some embodiments, the energy-related signals captured by the plurality of metering devices include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages. In embodiments, the energy-related signals may include (or leverage) substantially any electrical parameter derived from voltage and current signals (including the voltages and currents themselves), for example.

In some embodiments, processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices in the electrical system to generate or update a plurality of dynamic tolerance curves, includes: processing electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices at a first, initial time to generate a dynamic tolerance curve for each respective metering point of a plurality of metering points in the electrical system based on a response characteristic of the electrical system at each respective metering point at the first, initial time. Electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices at subsequent times after the first, initial time may be continued to be processed to optimize the dynamic tolerance curve for each respective metering point based on a response characteristic of the electrical system at each respective metering point at the subsequent times.

In some embodiments, degradations or improvements in the electrical system's sensitivity or resilience to power quality events at each respective metering point may be identified and reported. In one aspect, the degradations or improvements are identified based identified changes in the dynamic tolerance curve for each respective metering point. Additionally, in one aspect the identified degradations or improvements are reported by: generating and/or initiating a warning indicating the identified degradations or improvements in the electrical system's sensitivity or resilience to power quality events, and communicating the warning via at least one of: a report, a text, an email, audibly, and an interface of a screen/display. In some embodiments, the warning provides actionable recommendations for responding to the identified degradations or improvements in the electrical system's sensitivity or resilience to power quality events.

In some embodiments, selectively aggregating power quality data from the plurality of dynamic tolerance curves to analyze power quality events, includes: selectively aggregating power quality data from the plurality of dynamic tolerance curves to generate at least one aggregate dynamic tolerance curve, and analyzing the at least one aggregated dynamic tolerance curve to consider power quality events in the electrical system. In one aspect, the power quality data is selectively aggregated based on locations of the plurality of metering points in the electrical system. Additionally, in one aspect the power quality data is selectively aggregated based on criticality or sensitivity of the plurality of metering points to power quality events.

In some embodiments, selectively aggregating power quality data from the plurality of dynamic tolerance curves to analyze power quality events, includes: determining if any discrepancies exist between the selectively aggregated power quality data, and requesting additional information from a system user to reconcile the discrepancies. The discrepancies may include, for example, inconsistent naming conventions in the selectively aggregated data.

In some embodiments, selectively aggregating power quality data from the at least one dynamic tolerance curve to consider power quality events, includes: determining a relative criticality score of each of the power quality events to a process or an application associated with the electrical system. In one aspect, the relative criticality score is based on an impact of the power quality events to the process or the application. In one aspect, the impact of the power quality events is related to tangible or intangible costs associated with the power quality events to the process or the application. Additionally, in one aspect the impact of the power quality events is related to relative impact on loads in the electrical system. In some embodiments, the determined relative criticality score may be used to prioritize responding to the power quality events.

In some embodiments, power quality events in each of the plurality of dynamic tolerance curves may be tagged with relevant and characterizing information based on information extracted about the power quality events. Examples of relevant and characterizing information may include at least one of: severity (magnitude), duration, power quality type (e.g., sag, swell, interruption, oscillatory transient, impulsive transient, etc.), time of occurrence, process(es) involved, location, devices impacted, relative or absolute impact, recovery time, periodicity of events or event types, etc. Additionally, examples of relevant and characterizing information may include at least one of: information about activities occurring before, during, or after the power quality events, such as a measured load change correlating with an event, or a particular load or apparatus switching on or off before, during or after the event. In some embodiments, the information about the power quality events may be extracted from portions of the electrical measurement data taken prior to a start time of the power quality events, and from portions of the electrical measurement data taken after a conclusion of the power quality events. Additionally, in some embodiments the information about the power quality events may be extracted from portions of the electrical measurement data taken during the power quality events.

In some embodiments, processing electrical measurement data from or derived from energy-related signals captured by a plurality of metering devices in the electrical system to generate or update a plurality of dynamic tolerance curves, includes: receiving electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices in the electrical system during a first-time period correspond to a learning period. One or more upper alarm thresholds and one or more lower alarm thresholds may be generated for each of the plurality of dynamic tolerance curves based on the electrical measurement data captured during the first-time period. Electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices during a second-time period corresponding to a normal-operation period may be received, and it may be determined if at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds needs to be updated based on the electrical measurement data received during the second-time period. In response to determining that at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds needs to be updated, the at least one of the one or more upper alarm thresholds and the one or more lower alarm thresholds may be updated.

In some embodiments, the one or more upper alarm thresholds include a threshold above a nominal or expected voltage at the point of installation of the respective one of the plurality of metering devices in the electrical system. In one aspect, the threshold above the nominal voltage is indicative of transients, swells, or overvoltages. In some embodiments, the one or more lower alarm thresholds include a threshold below a nominal or expected voltage at the point of installation of the respective one of the plurality of metering devices in the electrical system. In one aspect, the threshold below the nominal voltage is indicative of sags, interruptions, notches, or undervoltages. In some embodiments, each of the one or more upper alarm thresholds and each of the one or more lower alarm thresholds has an associated magnitude and duration.

In some embodiments, each of the plurality of metering devices in the electrical system is associated with a respective one of the plurality of metering points. Additionally, in some embodiments at least one of the plurality of metering devices includes a virtual meter. In some embodiments, the plurality of dynamic tolerance curves may be selectively displayed in at least one of: a graphical user interface (GUI) of a control system used for controlling one or more parameters associated with the electrical system, and a GUI of at least one of the plurality of metering devices.

In some embodiments, dynamic tolerance curves according to the disclosure can warn a user of any (or substantially any) degradation or improvement of the electrical system's sensitivity or resilience (e.g., if the impact is defined by the time to get back to normal). This can be done either on a new calculated optimal model, or on a more traditional key performance indicator (KPI), such as the evolution of the percentage of loads dropped off.

Typically an electrical system's sensitivity or resilience does not stay at exactly the same level (or levels) over time. Degradations may occur, but also improvements due, for example, to normal life operations, changes in equipment, maintenance operations, time of impact, etc. A warning indicating a degradation or improvement can be provided within an existing live system (such as a Power or Industrial/manufacturing SCADA system, a building management system, a Power Monitoring system). As another example, a specific report may be issued and sent via email or provided online on a mobile application.

In these cases, specific meter related actionable recommendations may be sent to a user or a partner or a services team to provide them with optional or compulsory actions to perform.

In another aspect of this disclosure, a system for analyzing power quality events in an electrical system includes at least one input coupled to at least a plurality of metering devices in the electrical system, and at least one output coupled to at least a plurality of loads monitored by the plurality of metering devices. The system for managing power quality events also includes a processor coupled to receive electrical measurement data from or derived from energy-related signals captured by the plurality of metering devices from the at least one system input. The processor may be configured to process the electrical measurement data to generate or update at least one of a plurality of dynamic tolerance curves. In some embodiments, at least one of a plurality of dynamic tolerance curves characterizes and/or depicts a response characteristic of the electrical system at an at least respective metering point of a plurality of metering points in the electrical system. The processor may also be configured to selectively aggregate power quality data from the plurality of dynamic tolerance curves, and analyze power quality events in the electrical system based on the selectively aggregated power quality data. The processor may be further configured to adjust at least one parameter associated with one or more of the plurality of loads in response to the power quality events analyzed. In some embodiments, the at least one parameter is adjusted in response to a control signal generated at the at least one system output and provided to the one or more of the plurality of loads.

In some embodiments, the energy-related signals captured by the plurality of metering devices include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages.

In some embodiments, the metering devices (e.g., IEDs) and loads of the above and below described systems and methods are installed, located or derived from different respective locations (i.e., a plurality of locations) or metering points in the electrical system. A particular IED (e.g., a second IED) may be upstream from another IED (e.g., a third IED) in the electrical system while being downstream from a further IED (e.g., a first IED) in the electrical system, for example.

As used herein, the terms “upstream” and “downstream” are used to refer to electrical locations within an electrical system. More particularly, the electrical locations “upstream” and “downstream” are relative to an electrical location of an IED collecting data and providing this information. For example, in an electrical system including a plurality of IEDs, one or more IEDs may be positioned (or installed) at an electrical location that is upstream relative to one or more other IEDs in the electrical system, and the one or more IEDs may be positioned (or installed) at an electrical location that is downstream relative to one or more further IEDs in the electrical system. A first IED or load that is positioned on an electrical circuit upstream from a second IED or load may, for example, be positioned electrically closer to an input or source of the electrical system (e.g., a utility feed) than the second IED or load. Conversely, a first IED or load that is positioned on an electrical circuit downstream from a second IED or load may be positioned electrically closer to an end or terminus of the electrical system than the other IED.

A first IED or load that is electrically connected in parallel (e.g., on an electrical circuit) with a second IED or load may be considered to be “electrically” upstream from said second IED or load in embodiments, and vice versa. In embodiments, algorithm(s) used for determining a direction of a power quality event (i.e., upstream or downstream) is/are located (or stored) in the IED, cloud, on-site software, gateway, etc. As one example, the IED can record an electrical event's voltage and current phase information (e.g., by sampling the respective signals) and communicatively transmit this information to a cloud-based system. The cloud-based system may then analyze the voltage and current phase information (e.g., instantaneous, root-mean-square (rms), waveforms and/or other electrical characteristic) to determine if the source of the voltage event was electrically upstream or downstream from where the IED is electrically coupled to the electrical system (or network).

As used herein, a load loss (sometimes also referred to as a “loss of load”) is the unexpected, unplanned and/or unintentional removal of one or more loads from the electrical system. In this application, a voltage perturbation or event, and the subsequent load loss, is likely a result of one or more external influences on the electrical system (e.g., a fault, etc.), or the normal or abnormal operation of loads, protective devices, mitigation devices, and/or other equipment intentionally connected to the electrical system. Load losses may be indicated by measured parameters such as voltage, current, power, energy, harmonic distortion, imbalance, etc., or they may be indicated by discrete (digital) and/or analog input-output (I/O) signals originating from equipment directly and/or indirectly connected to the electrical system. For example, breakers often provide an output indication on their present position (e.g., open/closed, off/on, etc.) to communicate their operational status.

In some embodiments, the electrical measurement data from energy-related signals captured by the plurality of metering devices may be processed on one or more or the plurality of metering devices, or be processed in on-site software, in a cloud-based application, or in a gateway, etc., to characterize power quality events in the electrical system. Additionally, in some embodiments the electrical measurement data may be processed on a system for quantifying power quality events in an electrical system, for example, a control system associated with the electrical system. The control system may be used for controlling one or more parameters associated with the electrical system, for example. In embodiments, identifying the power quality event may include identifying: (a) a type of power quality event, (b) a magnitude of the anomalous power quality event, (c) a duration of the power quality event, and/or (d) a location of the power quality event in the electrical system. In embodiments, the power quality event type may include one of a voltage sag, a voltage swell, a voltage interruption, and a voltage transient. Additionally, in embodiments the location of the power quality event may be derived from voltage and current signals as measured by the IEDs and associated with the anomalous voltage condition.

As discussed above, a voltage event is one example type of power quality event. A power quality event may include at least one of a voltage sag, a voltage swell, and a voltage transient, for example. According to IEEE Standard 1159-2009, for example, a voltage sag is a decrease to between 0.1 and 0.9 per unit (pu) in rms voltage or current at the power frequency for durations of 0.5 cycle to 1 min. Typical values are 0.1 to 0.9 pu. Additionally, according to IEEE Standard 1159-2009, a voltage swell is an increase in rms voltage or current at the power frequency for durations from 0.5 cycles to 1 min. Below is a table from IEEE Standard 1159-2009 (known art), which defines various categories and characteristics of power system electromagnetic phenomena.

Typical spectral Typical Typical voltage Categories content duration magnitude 1.0 Transients 1.1 Impulsive 1.1.1 Nanosecond 5 μs rise <50 ns 1.1.2 Microsecond 1 μs rise 50 ns-1 ms 1.1.3 Millisecond 0.1 ms rise >1 ms 1.2 Oscillatory 1.2.1 Low frequency <5 kHz 0.3-50 ms 0-4 pu 1.2.2 Medium frequency 5-500 kHz 20 μs 0-8 pu 1.2.3 High frequency 0.5-5 MHz 5 μs 0-4 pu 2.0 Short duration variations 2.1 Instantaneous 2.1.1 Sag 0.5-30 cycles 0.1-0.9 pu 2.1.2 Swell 0.5-30 cycles 1.1-1.8 pu 2.2 Momentary 2.2.1 Interruption 0.5 cycles-3 s  <0.1 pu 2.2.2 Sag 30 cycles-3 s 0.1-0.9 pu 2.2.3 Swell 30 cycles-3 s 1.1-1.4 pu 2.3 Temporary 2.3.1 Interruption 3 s-1 min <0.1 pu 2.3.2 Sag 3 s-1 min 0.1-0.9 pu 2.3.3 Swell 3 s-1 min 1.1-1.2 pu 3.0 Long duration variations 3.1 Interruption, sustained >1 min 0.0 pu 3.2. Undervoltages >1 min 0.8-0.9 pu 3.3 Overvoltages >1 min 1.1-1.2 pu 4.0 Voltage imbalance steady state 0.5-2% 5.0 Waveform distortion 5.1 DC offset steady state 0-0.1% 5.2 Harmonics 0-100th H steady state  0-20% 5.3 Interharmonics 0-6 kHz steady state   0-2% 5.4 Notching steady state 5.5 Noise broad-band steady state   0-1% 6.0 Voltage fluctuations <25 Hz intermittent 0.1-7% 7.0 Power frequency variations <10 s

It is understood that the above table is one standards body's (IEEE in this case) way of defining/characterizing power quality events. It is understood there are other standards that define power quality categories/events as well, such as the International Electrotechnical Commission (IEC), American National Standards Institute (ANSI), etc., which may have different descriptions or power quality event types, characteristics, and terminology. In embodiments, power quality events may be customized power quality events (e.g., defined by a user).

In some embodiments, the electrical measurement data processed to identify the power quality event may be continuously or semi-continuously captured by the plurality of metering devices, and the tolerance curves may be dynamically updated in response to power quality events detected (or identified) from the electrical measurement data. For example, the tolerance curve may initially be generated in response to power quality events identified from electrical measurement data captured at a first time, and may be updated or revised in response to (e.g., to include or incorporate) power quality events identified from electrical measurement data captured at a second time. As events are captured, the tolerance curve (also sometimes referred to herein as “a dynamic tolerance curve”) may be continuously (e.g., dynamically) updated according to the unique response of the electrical system.

In some embodiments, the tolerance curve may be displayed in a GUI of at least one IED, or a GUI of a control system used for monitoring or controlling one or more parameters associated with the electrical system. In embodiments, the control system may be a meter, an IED, on-site/head-end software (i.e., a software system), a cloud-based control system, a gateway, a system in which data is routed over the Ethernet or some other communications system, etc. A warning may be displayed in the GUI of the IED, the monitoring system or the control system, for example, in response to a determined impact (or severity) of the power quality event being outside of a range or threshold. In some embodiments, the range is a predetermined range, for example, a user configured range. Additionally, in some embodiments the range is automatic, for example, using standards-based thresholds. Further, in some embodiments the range is “learned,” for example, by starting with a nominal voltage and pushing out the thresholds as non-impactful events occur in the natural course of the electrical network's operation.

The GUI may be configured to display factors contributing to the power quality event. Additionally, the GUI may be configured to indicate a location of the power quality event in the electrical system. Further, the GUI may be configured to indicate how loads (or another specific system or piece of equipment in the electrical system) will respond to the power quality event. It is understood that any number of information may be displayed in the GUI. As part of this invention, any electrical parameter, impact to a parameter, I/O status input, I/O output, process impact, recovery time, time of impact, phases impacted, potentially discrete loads impacted beneath a single IED, etc. may be displayed in the GUI. FIG. 20, for example, as will be discussed further below, shows a simple example of incorporating percent load impacted with an indication of recovery time.

In embodiments, the tolerance curve displayed in the GUI does not have fixed scaling but, rather, can (and needs to) auto-scale, for example, to capture or display a plurality of power quality events. In accordance with various aspects of the disclosure, the beauty of having a dynamic tolerance curve is not being constrained to a static curve or curves (e.g., with fixed scaling). For example, referring briefly to FIG. 2 (which will be discussed further below), while the y-axis is shown as a percent of nominal in FIG. 2, it can also be shown as an absolute nominal value (e.g., 120 volts, 208 volts, 240 volts, 277 volts, 480 volts, 2400 volts, 4160 volts, 7.2 kV, 12.47 kV, etc.). In this case, auto-scaling would be required because different voltage ranges would require different scaling for the y-axis. Additionally, the x-axis may be scaled in different units (e.g., cycles, seconds, etc.) and/or may have a variable maximum terminus point (e.g., 10 seconds, 1 minute, 5 minutes, 600 cycles, 3600 cycles, 18,000 cycles, etc.). In other words, in some embodiments there is no reason for the GUI to show more than it has to.

In embodiments, a goal of the invention claimed herein is to build a customized tolerance curve for a discrete location within a customer's power system (e.g., at a given IED) based on a perceived impact to downstream loads. Additionally, in embodiments a goal of the invention claimed herein is to quantify the time it takes to recover from a power quality event. In short, aspects of the invention claimed herein are directed toward describing the impact of a power quality event, which allows a customer to understand their operational parameters and constraints, accordingly.

As used herein, an IED is a computational electronic device optimized to perform a particular function or set of functions. As discussed above, examples of IEDs include smart utility meters, power quality meters, and other metering devices. IEDs may also be imbedded in variable speed drives (VSDs), uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical apparatus. IEDs may be used to perform monitoring and control functions in a wide variety of installations. The installations may include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing co-location centers, data centers, power distribution networks, and the like. For example, where the IED is an electrical power monitoring device, it may be coupled to (or be installed in) an electrical power distribution system and configured to sense and store data as electrical parameters representing operating characteristics (e.g., voltage, current, waveform distortion, power, etc.) of the power distribution system. These parameters and characteristics may be analyzed by a user to evaluate potential performance, reliability or power quality-related issues. The IED may include at least a controller (which in certain IEDs can be configured to run one or more applications simultaneously, serially, or both), firmware, a memory, a communications interface, and connectors that connect the IED to external systems, devices, and/or components at any voltage level, configuration, and/or type (e.g., AC, DC). At least certain aspects of the monitoring and control functionality of a IED may be embodied in a computer program that is accessible by the IED.

In some embodiments, the term “IED” as used herein may refer to a hierarchy of IEDs operating in parallel and/or tandem. For example, a IED may correspond to a hierarchy of energy meters, power meters, and/or other types of resource meters. The hierarchy may comprise a tree-based hierarchy, such a binary tree, a tree having one or more child nodes descending from each parent node or nodes, or combinations thereof, wherein each node represents a specific IED. In some instances, the hierarchy of IEDs may share data or hardware resources and may execute shared software.

The features proposed in this disclosure evaluate specific power quality events to characterize their impact on loads of an electrical system, recovery time, and other useful or interesting parameters. Its scope may include discrete metered points, network zones, and/or the aggregated electrical system in total. Novel ideas to display these concepts are also discussed, allowing the energy consumer to more efficiently and cost-effectively identify, analyze, mitigate, and manage their electrical networks.

Of the seven recognized power quality categories defined by IEEE 1159-2009, short-duration root mean square (rms) variations are generally the most disruptive and have the largest universal economic impact on energy consumers. Short-duration rms variations include voltage sags/dips, swells, instantaneous interruptions, momentary interruptions and temporary interruptions. One example study by the Electric Power Research Institute (EPRI) estimates an average of about 66 voltage sags are experienced by industrial customers each year. As the trend of industries becoming more dependent on sag-sensitive equipment has increased, so has the impact of these events.

The prevalence of voltage sags and the consequences of a growing install base of sag-sensitive equipment present many additional opportunities for electric solutions and services providers. The table below illustrates several example opportunities:

Opportunities Benefits Solutions Increased monitoring systems components More suitable sag-immunity equipment Targeted sag mitigation equipment Services Engineering and consulting Remote monitoring and diagnostics Equipment installation

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the disclosure, as well as the disclosure itself may be more fully understood from the following detailed description of the drawings, in which:

FIG. 1 shows a graphical view of several example power quality categories;

FIG. 1A shows an example electrical system in accordance with embodiments of the disclosure;

FIG. 1B shows an example intelligent electronic device (IED) that may be used in an electrical system in accordance with embodiments of the disclosure;

FIG. 2 shows an example Information Technology Industry (ITI) curve (also sometimes referred to as a “power acceptability curve”);

FIG. 3 shows an example baseline voltage tolerance curve which could be the ITI curve (as illustrated) or some other unique relationship between an event's voltage magnitude and duration;

FIG. 4 shows an example voltage sag event on a baseline voltage tolerance curve;

FIG. 5 shows an example recommended change to the baseline voltage tolerance curve of FIG. 3 based on an impact of the voltage sag event shown in FIG. 4;

FIG. 6 shows an example dynamically customized and updated voltage tolerance curve;

FIG. 7 shows an example of a multitude of impactful and non-impactful voltage sags, swells, and transients on a voltage tolerance curve;

FIG. 8 shows a dynamically customized and updated voltage tolerance curve for a multitude of impactful and non-impactful events;

FIG. 9 shows an example three-dimensional (3-D) tolerance-impact curve with load(s) impact;

FIG. 10 shows an example 3-D tolerance-impact curve with gradient color shading indicating severity of load(s) impact;

FIG. 11 shows an example 3-D tolerance-impact curve with a sample event indicating severity of load(s) impact;

FIG. 12 shows an example 3-D tolerance-impact curve with recovery time;

FIG. 13 shows an example 3-D tolerance-impact curve with gradient color shading indicating length of recovery time;

FIG. 14 shows an example 3-D tolerance-impact curve with a sample event indicating length of recovery time;

FIG. 15 shows another example 3-D tolerance-impact curve with a sample event indicating production losses as an economic impact;

FIG. 16 shows an example simple electrical network with a fault;

FIG. 16A shows another example electrical network with a fault;

FIG. 17 shows an example customized tolerance curve with a multitude of impactful and non-impactful upstream and downstream events;

FIG. 18 shows an example customized tolerance curve with a multitude of impactful and non-impactful disaggregated upstream events;

FIG. 19 shows an example customized tolerance curve with a multitude of impactful and non-impactful disaggregated downstream events;

FIG. 20 shows an example 3-D tolerance-impact curve with load impact, recovery time and upstream/downstream event sources indicated for a multitude of events;

FIG. 21 is a diagram showing an example progression of costs to mitigate voltage events;

FIG. 22 shows an example customized and updated tolerance curve for the voltage sag event illustrated in FIG. 4;

FIG. 23 shows the SEMI F47 curve superimposed on the plot illustrated in FIG. 22;

FIG. 24 shows example ride-through benefits of a sag mitigation device in an electrical system, one example of which is SagFighter® by Schneider Electric;

FIG. 25 shows an example of a multitude of potentially avoided load impact events with a sag mitigation device;

FIG. 26 shows another example of a multitude of potentially avoided load impacting events and their aggregated recovery time with a sag mitigation device;

FIG. 27 shows an example of the predicted impact of installing a voltage event mitigation device;

FIG. 28 shows an example of the actual impact of installing a voltage event mitigation device;

FIG. 29 shows an example of a simple electrical system with a plurality of IEDs;

FIG. 30 shows an example recovery timeline for a plurality of IED types experiencing a voltage event;

FIG. 30A illustrates an example of virtual metering being used to identify an impact of a voltage event on unmetered loads;

FIG. 30B shows an example electrical system in accordance with embodiments of this disclosure;

FIGS. 30C-30E show example dynamic tolerance curves in accordance with embodiments of this disclosure;

FIG. 30E-30I show further example electrical systems in accordance with embodiments of this disclosure;

FIG. 31 shows an example fault on the simple electrical system of FIG. 29;

FIG. 32 shows example zones of the simple electrical system of FIG. 29, for example, based on step-down transformer locations;

FIG. 33 shows an example customized zone configuration of the simple electrical system of FIG. 29;

FIG. 34 shows an example of a simple voltage tolerance curve (also sometimes referred to as a power acceptability curve);

FIG. 35 shows an example voltage sag event shown on the simple voltage tolerance curve of FIG. 34;

FIG. 36 shows an example updated voltage tolerance curve after the voltage sag event illustrated in FIG. 35;

FIG. 37 shows an example second voltage sag event on the voltage tolerance curve illustrated in FIG. 36;

FIG. 38 shows an example updated voltage tolerance curve after the second voltage sag event illustrated in FIG. 37;

FIG. 39 shows a third example voltage sag event on the voltage tolerance curve illustrated in FIG. 38;

FIG. 40 shows an example voltage tolerance curve after the third voltage sag event illustrated in FIG. 39;

FIG. 41 is a plot showing measured load(s) versus time for an example impactful voltage event;

FIG. 42 is a plot showing measured load(s) versus time for multiple example impactful voltage events;

FIG. 43 is a plot showing measured, typical and expected load(s) versus time for an example voltage event;

FIG. 44 is a plot showing percent load impact versus time;

FIG. 45 is a flowchart illustrating an example method for managing power quality events (or disturbances) in an electrical system;

FIG. 46 is a flowchart illustrating an example method for quantifying power quality events (or disturbances) in an electrical system;

FIG. 47 is a flowchart illustrating an example method for expanded qualified lead generation for power quality;

FIG. 48 is a flowchart illustrating an example method for generating a dynamic tolerance curve for power quality;

FIG. 49 shows an illustrative waveform;

FIG. 50 shows another illustrative waveform;

FIG. 51 is a flowchart illustrating an example method for characterizing power quality events in an electrical system;

FIG. 52 is a flowchart illustrating an example method for characterizing an impact of a power quality event on an electric system;

FIG. 53 is a flowchart illustrating an example method for reducing recovery time from a power quality event in an electrical system, for example, by tracking a response characteristic of the electrical system.

FIG. 54 is a flowchart illustrating an example method for analyzing power quality events in an electrical system;

FIG. 55 is a flowchart illustrating an example method for generating dynamic tolerance curves, and FIG. 55A shows an example method for moving from individual meters thresholds calculation to groups of meters thresholds calculation in accordance with embodiments of this disclosure;

FIG. 56 is a flowchart illustrating an example method for tagging criticality scores, for example, during a learning period;

FIG. 57 is a flowchart illustrating another example method for tagging criticality scores, for example, during a learning period;

FIGS. 57A-57K illustrate several example ways in which loss of load may be modeled in accordance with embodiments of this disclosure;

FIG. 58 is a flowchart illustrating an example method for generating dynamic tolerance curves, for example, after a learning period;

FIG. 59 is a flowchart illustrating an example method for leveraging and combining dynamic tolerance curves;

FIG. 60 illustrates an example dynamic tolerance curve;

FIG. 61 illustrates another example dynamic tolerance curve;

FIG. 62 illustrates a further example dynamic tolerance curve;

FIG. 63 illustrates another example dynamic tolerance curve;

FIG. 64 illustrates a further example dynamic tolerance curve;

FIGS. 65-65C illustrate example arrangements of metering devices in an electrical system in accordance with various embodiments of this disclosure;

FIG. 66 illustrates another example dynamic tolerance curve;

FIG. 67 illustrates a further example dynamic tolerance curve;

FIG. 68 illustrates example contactor characteristics;

FIG. 69 illustrates another example dynamic tolerance curve;

FIG. 70 illustrates an example boundary line in accordance with embodiments of this disclosure;

FIG. 71 illustrates another example boundary line in accordance with embodiments of this disclosure;

FIG. 72 illustrates a further example boundary line in accordance with embodiments of this disclosure;

FIG. 73 illustrates a chart of example threshold calculations for individual meters in an electrical system in accordance with embodiments of this disclosure;

FIG. 74 illustrates a chart of example threshold calculations for groups of meters in an electrical system, for example, based on criticality groups in accordance with embodiments of this disclosure;

FIG. 75 illustrates a chart reflecting an example in which thresholds may be manually adjusted in response to usage and/or user/process impact analysis, e.g., by type of load/process in accordance with embodiments of this disclosure; and

FIG. 76 illustrates a chart reflecting an example in which thresholds may be automatically adjusted, for example, in response to recovery duration in accordance with embodiments of this disclosure.

DETAILED DESCRIPTION

The features and other details of the concepts, systems, and techniques sought to be protected herein will now be more particularly described. It will be understood that any specific embodiments described herein are shown by way of illustration and not as limitations of the disclosure and the concepts described herein. Features of the subject matter described herein can be employed in various embodiments without departing from the scope of the concepts sought to be protected.

For convenience, certain introductory concepts and terms used in the specification (and adopted from IEEE Standard 1159-2009) are collected here. Several of these concepts and terms are shown in FIG. 1, for example. It is notable that FIG. 1 does not include all power quality categories such as waveform distortion, imbalance, voltage fluctuations, and power frequency deviations.

As used herein, the term “aperiodic event” is used to describe an electrical event that occurs non-cyclically, arbitrarily or without specific temporal regularity. For the sake of this paper, both short-duration root-mean-square (rms) variations and transients are considered to be aperiodic events (i.e., notching is considered as a harmonic phenomenon).

As used herein, the term “instantaneous interruption” is used to describe a deviation to 0-10% of the nominal value for a duration of ½ cycle to 30 cycles.

As used herein, the term “momentary interruption” is used to describe a deviation to 0-10% of the nominal value for a duration of 30 cycles to 3 seconds.

As used herein, the term “sag” (of which a “voltage sag” is one example) is used to describe a deviation to 10-90% of the nominal value, for example, for a duration of ½ cycle to 1 minute, as shown in FIG. 1.

As used herein, the term “short-duration rms variations” is used to describe a deviation from the nominal value with a duration of ½ cycle to 1 minute. Sub-categories of short-duration rms variations include instantaneous interruptions, momentary interruptions, temporary interruptions, sags and swells.

As used herein, the term “swell” is used to describe a deviation greater than 110% of the nominal value, for example, for a duration of ½ cycle to 1 minute, as shown in FIG. 1.

As used herein, the term “temporary interruption” is used to describe a deviation to 0-10% of the nominal value for a duration of 3 seconds to 1 minute.

As used herein, the term “transient” is used to describe a deviation from the nominal value with a duration less than 1 cycle. Sub-categories of transients include impulsive (uni-direction polarity) and oscillatory (bi-directional polarity) transients.

In embodiments, the degree of impact a short-duration rms variation has on an energy consumer's facility is primarily dependent on four factors:

-   -   1. The nature and source of the event,     -   2. The susceptibility of the load(s) to the event,     -   3. The event's influence on the process or activity, and     -   4. The cost sensitivity to this event.

Consequently, each customer system, operation or load may respond differently to a given electrical perturbation. For example, it is possible for a voltage sag event to significantly impact one customer's operation while the same voltage sag may have little or no noticeable impact on another customer's operation. It is also possible for a voltage sag to impact one part of a customer's electrical system differently than it does another part of the same electrical system.

Referring to FIG. 1A, an example electrical system in accordance with embodiments of the disclosure includes one or more loads (here, loads 111, 112, 113, 114, 115) and one or more intelligent electronic devices (IEDs) (here, IEDs 121, 122, 123, 124) capable of sampling, sensing or monitoring one or more parameters (e.g., power monitoring parameters) associated with the loads. In embodiments, the loads 111, 112, 113, 114, 115 and IEDs 121, 122, 123, 124 may be installed in one or more buildings or other physical locations or they may be installed on one or more processes and/or loads within a building. The buildings may correspond, for example, to commercial, industrial or institutional buildings.

As shown in FIG. 1A, the IEDs 121, 122, 123, 124 are each coupled to one or more of the loads 111, 112, 113, 114, 115 (which may be located “upstream” or “downstream” from the IEDs in some embodiments). The loads 111, 112, 113, 114, 115 may include, for example, machinery or apparatuses associated with a particular application (e.g., an industrial application), applications, and/or process(es). The machinery may include electrical or electronic equipment, for example. The machinery may also include the controls and/or ancillary equipment associated with the equipment.

In embodiments, the IEDs 121, 122, 123, 124 may monitor and, in some embodiments, analyze parameters (e.g., energy-related parameters) associated with the loads 111, 112, 113, 114, 115 to which they are coupled. The IEDs 121, 122, 123, 124 may also be embedded within the loads 111, 112, 113, 114, 115 in some embodiments. According to various aspects, one or more of the IEDs 121, 122, 123, 124 may be configured to monitor utility feeds, including surge protective devices (SPDs), trip units, active filters, lighting, IT equipment, motors, and/or transformers, which are some examples of loads 111, 112, 113, 114, 115, and the IEDs 121, 122, 123, 124 may detect ground faults, voltage sags, voltage swells, momentary interruptions and oscillatory transients, as well as fan failure, temperature, arcing faults, phase-to-phase faults, shorted windings, blown fuses, and harmonic distortions, which are some example parameters that may be associated with the loads 111, 112, 113, 114, 115. The IEDs 121, 122, 123, 124 may also monitor devices, such as generators, including input/outputs (I/Os), protective relays, battery chargers, and sensors (for example, water, air, gas, steam, levels, accelerometers, flow rates, pressures, and so forth).

According to another aspect, the IEDs 121, 122, 123, 124 may detect overvoltage and undervoltage conditions, as well as other parameters such as temperature, including ambient temperature. According to a further aspect, the IEDs 121, 122, 123, 124 may provide indications of monitored parameters and detected conditions that can be used to control the loads 111, 112, 113, 114, 115 and other equipment in the electrical system in which the loads 111, 112, 113, 114 and IEDs 121, 122, 123, 124 are installed. A wide variety of other monitoring and/or control functions can be performed by the IEDs 121, 122, 123, 124, and the aspects and embodiments disclosed herein are not limited to IEDs 121, 122, 123, 124 operating according to the above-mentioned examples.

It is understood that the IEDs 121, 122, 123, 124 may take various forms and may each have an associated complexity (or set of functional capabilities and/or features). For example, IED 121 may correspond to a “basic” IED, IED 122 may correspond to an “intermediate” IED, and IED 123 may correspond to an “advanced” IED. In such embodiments, intermediate IED 122 may have more functionality (e.g., energy measurement features and/or capabilities) than basic IED 121, and advanced IED 123 may have more functionality and/or features than intermediate IED 122. For example, in embodiments IED 121 (e.g., an IED with basic capabilities and/or features) may be capable of monitoring instantaneous voltage, current energy, demand, power factor, averages values, maximum values, instantaneous power, and/or long-duration rms variations, and IED 123 (e.g., an IED with advanced capabilities) may be capable of monitoring additional parameters such as voltage transients, voltage fluctuations, frequency slew rates, harmonic power flows, and discrete harmonic components, all at higher sample rates, etc. It is understood that this example is for illustrative purposes only, and likewise in some embodiments an IED with basic capabilities may be capable of monitoring one or more of the above energy measurement parameters that are indicated as being associated with an IED with advanced capabilities. It is also understood that in some embodiments the IEDs 121, 122, 123, 124 each have independent functionality.

In the example embodiment shown, the IEDs 121, 122, 123, 124 are communicatively coupled to a central processing unit 140 via the “cloud” 150. In some embodiments, the IEDs 121, 122, 123, 124 may be directly communicatively coupled to the cloud 150, as IED 121 is in the illustrated embodiment. In other embodiments, the IEDs 121, 122, 123, 124 may be indirectly communicatively coupled to the cloud 150, for example, through an intermediate device, such as a cloud-connected hub 130 (or a gateway), as IEDs 122, 123, 124 are in the illustrated embodiment. The cloud-connected hub 130 (or the gateway) may, for example, provide the IEDs 122, 123, 124 with access to the cloud 150 and the central processing unit 140.

As used herein, the terms “cloud” and “cloud computing” are intended to refer to computing resources connected to the Internet or otherwise accessible to IEDs 121, 122, 123, 124 via a communication network, which may be a wired or wireless network, or a combination of both. The computing resources comprising the cloud 150 may be centralized in a single location, distributed throughout multiple locations, or a combination of both. A cloud computing system may divide computing tasks amongst multiple racks, blades, processors, cores, controllers, nodes or other computational units in accordance with a particular cloud system architecture or programming. Similarly, a cloud computing system may store instructions and computational information in a centralized memory or storage, or may distribute such information amongst multiple storage or memory components. The cloud system may store multiple copies of instructions and computational information in redundant storage units, such as a RAID array.

The central processing unit 140 may be an example of a cloud computing system, or cloud-connected computing system. In embodiments, the central processing unit 140 may be a server located within buildings in which the loads 111, 112, 113, 114, 115, and the IEDs 121, 122, 123, 124 are installed, or may be remotely-located cloud-based service. The central processing unit 140 may include computing functional components similar to those of the IEDs 121, 122, 123, 124 is some embodiments, but may generally possess greater numbers and/or more powerful versions of components involved in data processing, such as processors, memory, storage, interconnection mechanisms, etc. The central processing unit 140 can be configured to implement a variety of analysis techniques to identify patterns in received measurement data from the IEDs 121, 122, 123, 124, as discussed further below. The various analysis techniques discussed herein further involve the execution of one or more software functions, algorithms, instructions, applications, and parameters, which are stored on one or more sources of memory communicatively coupled to the central processing unit 140. In certain embodiments, the terms “function”, “algorithm”, “instruction”, “application”, or “parameter” may also refer to a hierarchy of functions, algorithms, instructions, applications, or parameters, respectively, operating in parallel and/or tandem. A hierarchy may comprise a tree-based hierarchy, such a binary tree, a tree having one or more child nodes descending from each parent node, or combinations thereof, wherein each node represents a specific function, algorithm, instruction, application, or parameter.

In embodiments, since the central processing unit 140 is connected to the cloud 150, it may access additional cloud-connected devices or databases 160 via the cloud 150. For example, the central processing unit 140 may access the Internet and receive information such as weather data, utility pricing data, or other data that may be useful in analyzing the measurement data received from the IEDs 121, 122, 123, 124. In embodiments, the cloud-connected devices or databases 160 may correspond to a device or database associated with one or more external data sources. Additionally, in embodiments, the cloud-connected devices or databases 160 may correspond to a user device from which a user may provide user input data. A user may view information about the IEDs 121, 122, 123, 124 (e.g., IED makes, models, types, etc.) and data collected by the IEDs 121, 122, 123, 124 (e.g., energy usage statistics) using the user device. Additionally, in embodiments the user may configure the IEDs 121, 122, 123, 124 using the user device.

In embodiments, by leveraging the cloud-connectivity and enhanced computing resources of the central processing unit 140 relative to the IEDs 121, 122, 123, 124, sophisticated analysis can be performed on data retrieved from one or more IEDs 121, 122, 123, 124, as well as on the additional sources of data discussed above, when appropriate. This analysis can be used to dynamically control one or more parameters, processes, conditions or equipment (e.g., loads) associated with the electrical system.

In embodiments, the parameters, processes, conditions or equipment are dynamically controlled by a control system associated with the electrical system. In embodiments, the control system may correspond to or include one or more of the IEDs 121, 122, 123, 124 in the electrical system, central processing unit 140 and/or other devices within or external to the electrical system.

Referring to FIG. 1B, an example IED 200 that may be suitable for use in the electrical system shown in FIG. 1A, for example, includes a controller 210, a memory device 215, storage 225, and an interface 230. The IED 200 also includes an input-output (I/O) port 235, a sensor 240, a communication module 245, and an interconnection mechanism 220 for communicatively coupling two or more IED components 210-245.

The memory device 215 may include volatile memory, such as DRAM or SRAM, for example. The memory device 215 may store programs and data collected during operation of the IED 200. For example, in embodiments in which the IED 200 is configured to monitor or measure one or more electrical parameters associated with one or more loads (e.g., 111, shown in FIG. 1A) in an electrical system, the memory device 215 may store the monitored electrical parameters.

The storage system 225 may include a computer readable and writeable nonvolatile recording medium, such as a disk or flash memory, in which signals are stored that define a program to be executed by the controller 210 or information to be processed by the program. The controller 210 may control transfer of data between the storage system 225 and the memory device 215 in accordance with known computing and data transfer mechanisms. In embodiments, the electrical parameters monitored or measured by the IED 200 may be stored in the storage system 225.

The I/O port 235 can be used to couple loads (e.g., 111, shown in FIG. 1A) to the IED 200, and the sensor 240 can be used to monitor or measure the electrical parameters associated with the loads. The I/O port 235 can also be used to coupled external devices, such as sensor devices (e.g., temperature and/or motion sensor devices) and/or user input devices (e.g., local or remote computing devices) (not shown), to the IED 200. The I/O port 235 may further be coupled to one or more user input/output mechanisms, such as buttons, displays, acoustic devices, etc., to provide alerts (e.g., to display a visual alert, such as text and/or a steady or flashing light, or to provide an audio alert, such as a beep or prolonged sound) and/or to allow user interaction with the IED 200.

The communication module 245 may be configured to couple the IED 200 to one or more external communication networks or devices. These networks may be private networks within a building in which the IED 200 is installed, or public networks, such as the Internet. In embodiments, the communication module 245 may also be configured to couple the IED 200 to a cloud-connected hub (e.g., 130, shown in FIG. 1A), or to a cloud-connected central processing unit (e.g., 140, shown in FIG. 1A), associated with an electrical system including IED 200.

The IED controller 210 may include one or more processors that are configured to perform specified function(s) of the IED 200. The processor(s) can be a commercially available processor, such as the well-known Pentium™, Core™, or Atom™ class processors available from the Intel Corporation. Many other processors are available, including programmable logic controllers. The IED controller 210 can execute an operating system to define a computing platform on which application(s) associated with the IED 200 can run.

In embodiments, the electrical parameters monitored or measured by the IED 200 may be received at an input of the controller 210 as IED input data, and the controller 210 may process the measured electrical parameters to generate IED output data or signals at an output thereof. In embodiments, the IED output data or signals may correspond to an output of the IED 200. The IED output data or signals may be provided at I/O port(s) 235, for example. In embodiments, the IED output data or signals may be received by a cloud-connected central processing unit, for example, for further processing (e.g., to identify power quality events, as briefly discussed above), and/or by equipment (e.g., loads) to which the IED is coupled (e.g., for controlling one or more parameters associated with the equipment, as will be discussed further below). In one example, the IED 200 may include an interface 230 for displaying visualizations indicative of the IED output data or signals. The interface 230 may correspond to a graphical user interface (GUI) in embodiments, and the visualizations may include tolerance curves characterizing a tolerance level of the equipment to which the IED 200 is coupled, as will be described further below.

Components of the IED 200 may be coupled together by the interconnection mechanism 220, which may include one or more busses, wiring, or other electrical connection apparatus. The interconnection mechanism 220 may enable communications (e.g., data, instructions, etc.) to be exchanged between system components of the IED 200.

It is understood that IED 200 is but one of many potential configurations of IEDs in accordance with various aspects of the disclosure. For example, IEDs in accordance with embodiments of the disclosure may include more (or fewer) components than IED 200. Additionally, in embodiments one or more components of IED 200 may be combined. For example, in embodiments memory 215 and storage 225 may be combined.

Returning now to FIG. 1A, in order to accurately describe aperiodic events such as voltage sags in an electrical system (such as the electric system shown in FIG. 1A), it is important to measure the voltage signals associated with the event. Two attributes often used to characterize voltage sags and transients are magnitude (deviation from the norm) and duration (length in time) of the event. Both parameters are instrumental in defining, and thus, mitigating these types of power quality issues. Scatter plots of an event's magnitude (y-axis) versus its corresponding duration (x-axis) are typically shown in a single graph called a “Magnitude-Duration” plot, “Power Tolerance Curve”, or as referred to herein, a Tolerance Curve.

FIG. 2 illustrates a well-known Magnitude-Duration plot 250: the Information Technology Industry (ITI) Curve (sometimes referred to as an ITIC or CBEMA Curve) 260. The ITIC Curve 260 shows “an AC input voltage envelope which typically can be tolerated (no interruption in function) by most Information Technology Equipment (ITE),” and is “applicable to 120V nominal voltages obtained from 120V, 208Y/120V, and 120/240V 60 Hertz systems.” The “Prohibited Region” in the graph includes any surge or swell which exceeds the upper limit of the envelope. Events occurring in this region may result in damage to the ITE. The “No Damage Region” includes sags or interruptions (i.e., below the lower limit of the envelope) that are not expected to damage the ITE. Additionally, the “No Interruption in Function Region” describes the area between the blue lines where sags, swells, interruptions and transients can normally be tolerated by most ITE.

As is known, constraints of the ITIC Curve 260 include:

-   -   1. It is a static/fixed envelope/curve,     -   2. It is proposed for IT,     -   3. It is intended for 120V 60 Hz electrical systems,     -   4. It is a standardized/generic graph describing what “normally”         should be expected,     -   5. It inherently provides no information regarding the         consequences of an event,     -   6. It is solely a voltage-based graph, and does not consider any         other electrical parameter(s), and     -   7. It is presented on a semi-log graph for multiplicative         efficiency.

It is understood that prior art tolerance curves such as the ITIC/CBEMA, SEMI Curve, or other manually configured curves are often nothing more than suggestions for specific applications. They do not indicate how a specific system or piece of equipment, apparatus, load, or controls associated with the equipment, apparatus, or load will actually respond to a sag/swell event, what the event's impact will be the electrical system, or how and where to economically mitigate the issues. Furthermore, zones (sub-systems) within the electrical system are all treated the same, even though the majority of IEDs monitor multiple loads. A good analogy is a road atlas: while the atlas illustrates the location of the road, it does not indicate the location of road hazards, expected gas mileage, condition of the vehicle, construction, and so forth. A better approach is required to improve managing voltage sags and swells in electrical systems.

With the foregoing in mind, the ability to provide customized tolerance curves allows an energy consumer (and the systems and methods disclosed herein) to better manage their system through simplified investment decisions, reduced CAPEX and OPEX costs, identified and characterized issues and opportunities, improved event ride-though, and ultimately, higher productivity and profitability.

A few example factors to be considered when leveraging the benefits of providing dynamic tolerance curves for energy consumers include:

-   -   1. No two customers are exactly alike, and no two metering         points are identical. A dynamic tolerance curve is uniquely         customized to the point at which the metering data is collected         on a specific electrical system.     -   2. As events occur and are captured, a dynamic tolerance curve         is continuously updated according the unique responses of the         electrical system.     -   3. A dynamic tolerance curve can be applied to any type of         electrical system/any type of customer; it is not limited to ITE         systems.     -   4. A dynamic tolerance curve can also be used for substantially         any voltage level; it is not limited to 120-volt systems.     -   5. A dynamic tolerance curve does not have fixed scaling; it can         (and may need to) auto-scale.     -   6. It is possible to automatically aggregate dynamic tolerance         curves from discrete devices into a single dynamic system         tolerance curve.

With the foregoing in mind, there are a plurality of new potential features according to this disclosure that can produce numerous benefits for energy consumers. In embodiments, the goal of these features is to simplify a generally complex topic into actionable opportunities for energy consumers. Example features according to this disclosure are set forth below for consideration.

I. Dynamic Tolerance Curves

This embodiment of the disclosure comprises automatically adjusting a sag/swell tolerance curve based on load impact as measured by a discrete IED. In this embodiment, “load impact” is determined by evaluating a pre-event load against a post-event load (i.e., the load after the event's onset). The difference between the pre-event and post event loads (i.e., kW, current, energy, etc.) is used to quantify the event's impact. The measure of “impact” may be calculated as a percent value, absolute value, normalized value, or other value useful to the energy consumer. Further evaluations may include changes in voltage, current, power factor, total harmonic distortion (THD) levels, discrete harmonic component levels, total demand distortion (TDD), imbalance, or any other electrical parameter/characteristic that can provide an indication of the type (load or source), magnitude, and location of change within the electrical system. The source of data may originate from logged data, waveform data, direct MODBUS reads, or any other means.

FIG. 3 illustrates a typical tolerance curve (e.g., ITIC curve), which is used as a baseline (also shown in FIG. 2). It should be noted that in embodiments substantially any known uniquely described tolerance curve (e.g., SEMI F47, ANSI, CBEMA, other custom curve) may be used as the baseline tolerance curve because an intent of this embodiment of the disclosure is to dynamically customize (i.e., change, update, revise, etc.) the tolerance curve so that it reflects the unique electrical voltage event tolerance characteristics at the IED's point of installation. As more events are captured and quantified by the IED, the accuracy and characterization of the dynamic voltage tolerance curve may improve at that IED's point of installation. FIG. 3 is also shown as a semi-logarithmic graph; however, the dynamic tolerance curve may be scaled in any practical format for both analyses and/or viewing purposes.

FIG. 4 illustrates an example voltage sag event (50% of nominal, 3 milliseconds duration) on a standard/baseline tolerance curve that results in the loss of 20% of the load as determined by the IED. The shaded area in FIG. 5 illustrates the difference between the baseline tolerance curve (e.g., as shown in FIG. 3) and the actual tolerance of the downstream metered load(s) due to the particular sensitivity at this location in the electrical system to this degree (magnitude and duration) of voltage sag. FIG. 6 illustrates an example automatically customized and updated tolerance curve built from the event data point and determined for the point where the IED is installed on the electrical system. In embodiments, it assumes that any sag/swell/transient event with more severe characteristics (i.e., deeper voltage sag, greater voltage swells, larger transient, longer duration, etc.) will impact the load at least as severely as the event presently being considered.

FIG. 7 illustrates a multitude of voltage sags/swells/transients on a standard/baseline tolerance curve. Some events are indicated as impactful and some are indicated as not impactful, based on one or more changing parameters at the moment of the event. FIG. 8 illustrates an automatically customized and updated tolerance curve for the multitude of impactful and non-impactful voltage sags/swells/transients as determined by the measured data taken from the point where the IED is installed on the electrical system.

a. Three-Dimension (3-D) Dynamic Tolerance Curves with Load Impact (Also Sometimes Referred to Herein as “Dynamic Tolerance-Impact Curves”)

Standard tolerance curves (e.g., ITIC Curve, SEMI Curve, etc.) are described in two-dimensional graphs with percent of nominal voltage on the y-axis and duration (e.g., cycles, second, milliseconds, etc.) on the x-axis, for example, as shown in FIG. 7. While the y-axis is presented in units of percent of nominal voltage, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in FIG. 7, for example, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). These 2-D standard tolerance curve graphs provide only a limited description of an event's characteristics (magnitude and duration); they don't provide information related to an event's impact on the load(s). While the energy consumer knows an event occurred, they cannot tell whether (and if so, to what degree) an event impacted their electrical system (and likely, their operation).

Adding a third dimension to the tolerance curve graph allows the energy consumer to visually identify the characterization of their system's sag/swell/transient tolerance (at the metering point) related to magnitude, duration, and a third parameter such as load impact. Again, load impact is determined by analyzing changes in the load (or other electrical parameter) before and after an event using logged data, waveform data, direct MODBUS read data, other data, or any combination thereof.

Three-dimensional (3-D) tolerance curves in accordance with embodiments of the disclosure may be adapted and/or oriented to any axis, perspective, scale, numerically ascending/descending, alphabetized, color, size, shape, electrical parameter, event characteristic, and so forth to usefully describe an event or events to the energy consumer. For example, FIG. 9 illustrates an exemplary orthographic perspective of a tolerance-impact curve incorporating three parameters: 1) percent of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percent load impacted on the z-axis. While the y-axis is presented in units of percent of nominal voltage in the illustrated embodiment, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in the illustrated embodiment, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). FIG. 10 illustrates an exemplary single-point perspective 3-D view of the same tolerance-impact curve shown in FIG. 9, and incorporates the same respective parameters for the three axes. It also attempts to integrate color shading to help illustrate the severity of the impact due to specific magnitude and duration events (least to worst; yellow to red, in the illustrated embodiment). FIG. 11 attempts to illustrate an exemplary single-point perspective 3-D view of a tolerance-impact curve incorporating magnitude, duration, percent load impact, shading, and event shape (to provide more event characteristics in a single graph). Again, the load impact may be as a relative percentage of the total load before the event (as shown in the graph), as a real value (e.g., kW, Amps, etc.), ascending or descending in value, or any other manipulation of these or any other electrical parameters.

b. Three-Dimension (3-D) Dynamic Tolerance-Recovery Time Curves

Building on the previous section discussing load impact, in embodiments it is also possible to use tolerance-impact curves to more directly quantify the effect of a voltage sag/swell/transient event on an energy consumer's operation. The time to recover from an event may directly affect the overall cost of a voltage event.

For the purpose of this disclosure, “recovery time” is defined as the period of time required to return the electrical system parameters back to (or approximately back to) their original state prior to the event that prompted their initial perturbation. In embodiments, recovery time and load impact are independent variables; neither is dependent on the other. For example, a voltage event may impact a small percentage of load, yet the recovery time may be considerable. Conversely, the recovery time from an extremely impactful event could be relatively short. Just as the impact of an event is dependent on a number of factors (some examples of which are set forth in the summary section of this disclosure), so too is the recovery time. A few examples of factors that can heavily influence the duration of recovery time include: ability to quickly locate event source (if it's within the facility), extent of equipment damage, spare parts availability, personnel availability, redundant systems, protection schemes, and so forth.

One example method for calculating the recovery time includes measuring the elapsed time between the occurrence of a first impactful event and the point when the load exceeds a predetermined threshold of the pre-event load. For example, a 500 kW pre-event load with a 90% recovery threshold will indicate the recovery has occurred at 450 kW. If it takes 26 minutes for the metered load to meet or exceed 450 kW (i.e., 90% of the pre-event load), then the recovery time is equal to 26 minutes. The recovery threshold can be determined using a relative percentage of the pre-event load, an absolute value (kW), the recovery of the voltage or current levels, an external or manual trigger, a recognized standard value, a subjective configuration, or by some other method using an electrical or non-electrical parameter(s).

FIG. 12 illustrates an exemplary orthographic perspective of a tolerance-recovery time curve incorporating three parameters: 1) percent of nominal voltage on the y-axis, 2) duration in cycles and seconds (or alternatively, milliseconds) on the x-axis, and 3) recovery time or period in days, hours, and/or minutes on the z-axis. While the y-axis is presented in units of percent of nominal voltage in the illustrated embodiment, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in the illustrated embodiment, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). In embodiments, the z-axis (recovery time) may be configured to substantially any fixed scale (or auto-scaled), may be listed in ascending or descending order, and may use substantially any known temporal unit. FIG. 13 illustrates an exemplary single-point perspective 3-D view of the same tolerance-recovery time curve shown in FIG. 12, and incorporates the same respective parameters for the three axes. FIG. 13 also integrates color shading to help illustrate the severity of the recovery time due to specific magnitude and duration events (least to worst; yellow to red in the illustrated embodiment). FIG. 14 illustrates an exemplary single-point perspective 3-D view of a tolerance-recovery time curve incorporating magnitude, duration, recovery time, shading, and event shape (to provide more event characteristics in a single graph).

c. 3-D Dynamic Tolerance-Economic Impact Curves

The 3-D curves discussed above may also be used to illustrate economic impact (e.g., production losses, restart losses, product/material losses, equipment losses, third-party losses, total losses, etc.) as it relates to voltage events. Obviously, configuration may be time-consuming; however, the relationship between recovery time and any relevant economic factor can easily be shown and understood using dynamic tolerance-economic impact curves. The cost of downtime (CoD) may be initially used to determine a given economic cost during the recovery period (assuming the CoD value is reasonable). Some studies indicate each minute of downtime costs energy consumers in the automotive industry more than $22K. By contrast, the similar studies indicate that healthcare industry energy consumers lose more than $10K/minute of downtime. Over time, energy consumers (and the systems and methods disclosed herein) can quantify their typical recovery time costs (whether it's linear or non-linear), or they may have a study done to determine this relationship at their facility or business. Determining the relationship between voltage events and economic factors will allow energy consumers to make faster and better decisions regarding capitalization expenditures and/or the retention of services.

For example, FIG. 15 illustrates the production losses with respect to a 50% of nominal voltage sag event with a duration of 3 milliseconds. Assuming the recovery time was 8 hours (see, e.g., FIG. 13) and production losses are an average of $2.5K/hour, the total production losses will be $20K. If ride-through capabilities can help avoid an operational disruption at a cost of $50K, the payback for investing in voltage sag ride-though equipment is may only be about 2.5 voltage events, for example. As mentioned at the beginning of this document, studies have shown the average industrial customer experiences about 66 voltage sags each year so a decision to mitigate should be straightforward in this case.

d. Upstream/Downstream Tolerance-Impact Curves

As has been stated and is widely known, electrical systems are sensitive to voltage events in varying degrees. For some energy consumers, voltage events may just be a nuisance (no significant impact); for other energy consumers, any small voltage anomaly may be catastrophic. As previously discussed, quantifying the impact of voltage events helps energy consumers determine the severity, prevalence, and influence of these events on their operation. If voltage events impact the energy consumer's operation, the next step is identifying the source of the problem.

Metering algorithms and other associated methods may be used to determine whether a voltage event's source is upstream or downstream from a metering point (e.g., an IED's electrical point of installation in an electrical system). For example, FIG. 16 illustrates a simple electrical network with three metering points (M₁, M₂, and M₃). A fault (X) is shown to occur between M₁ and M₂. In embodiments, algorithms in M₁ may indicate the source of the fault to be downstream (↓) from its location, and algorithms in M₂ may indicate the source of the fault to be upstream (↑) from its location. Additionally, in embodiments algorithms in M₃ may indicate the source of the fault to be upstream (↑). By evaluating the fault as a system event (i.e., using data from all three IEDs), in embodiments it is possible to generally identify the location of the fault's source within the electrical network (i.e., with respect to the metering points).

This embodiment evaluates the impact of a voltage event against the indicated location (upstream or downstream from the metering point) related to the voltage event's source. This is very useful because upstream voltage event sources often require different mitigative solutions (and associated costs) than downstream voltage event sources. Furthermore, there will likely be different economic considerations (e.g., impact costs, mitigation costs, etc.) depending on where the voltage event source is located within the electrical system. The larger the impacted area, the more expensive the cost may be to mitigate the problem. Upstream voltage events can potentially impact a larger portion of the electrical network than downstream voltage events, and therefore, may be more expensive to mitigate. Again, the cost to mitigate voltage events will be determined on a case-by-case basis since each metering point is unique.

In embodiments, the IEDs installed at the metering points are configured to measure, protect, and/or control a load or loads. The IEDs are typically installed upstream from the load(s) because current flow to the load(s) may be a critical aspect in measuring, protecting and/or controlling the load(s). However, it is understood that the IEDs may also be installed downstream from the load(s).

Referring to FIG. 16A, another example electrical system includes a plurality of IEDs (IED1, IED2, IED3, IED4, IED5) and a plurality of loads (L1, L2, L3, L4, L5). In embodiments, loads L1, L2 correspond to a first load type, and loads L3, L4, L5 correspond to a second load type. The first load type may be the same as or similar to the second load type in some embodiments, or different from the second load type in other embodiments. Loads L1, L2 are positioned at a location that is “electrically” (or “conductively”) downstream relative to at least IEDs IED1, IED2, IED3 in the electrical system (i.e., IEDs IED1, IED2, IED3 are upstream from loads L1, L2). Additionally, loads L3, L4, L5 are positioned at a location that is “electrically” downstream relative to at least IEDs IED1, IED4, IED5 in the electrical system (i.e., IEDs IED1, IED4, IED5 are upstream from loads L3, L4, L5).

In the illustrated embodiment, a power quality event (or fault) X is shown occurring upstream relative to loads L1, L2. Up arrows indicate “upstream” and down arrows indicate “downstream” in the example embodiment shown. As illustrated, IEDs IED1, IED2 are shown pointing towards the fault X. Additionally, IEDs IED3, IED4, IED5 are shown pointing upstream. In embodiments, this is because the path leading to the fault X is upstream from IEDs IED3, IED4, IED5 respective location in the electrical system, and downstream from IEDs IED1, IED2 respective location in the electrical system. In embodiments, algorithms that determine a direction of the fault X may be located (or stored) in the IEDs, on-site software, cloud-based systems, and/or gateways, etc., for example.

FIG. 17 illustrates a 2-D graph voltage tolerance curve of voltage events captured by an IED similar to FIG. 7 above; however, the upstream and downstream voltage events are uniquely denoted and superimposed/overlaid together. FIG. 18 illustrates a 2-D voltage tolerance curve that shows only the upstream voltage events which are disaggregated from the total set of voltage events shown in FIG. 17. Similarly, FIG. 19 illustrates a 2-D voltage tolerance curve showing only the downstream voltage events as disaggregated from the total set of voltage events shown in FIG. 17. These graphs allow energy consumers (and the systems and methods disclosed herein) to distinguish the upstream events from the downstream events, thus, helping to provide a better visually intuitive view for identifying the primary location of voltage event sources (and perhaps, their causes). Of course, additional or alternative characteristics, parameters, filters, and/or other related information (e.g., electrical data, time, metadata, etc.) may be used, displayed and/or plotted to further effectively and productively embellish the voltage tolerance curves.

For example, FIG. 20 illustrates an exemplary orthographic perspective of a tolerance-impact-source location curve incorporating five parameters: 1) percent of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percent load impacted on the z-axis. While the y-axis is presented in units of percent of nominal voltage in the illustrated embodiment, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in the illustrated embodiment, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). Additional dimensions are also included in FIG. 20 such as the recovery time (size of data point) and whether a particular event was upstream or downstream from the metering point (data point center is white or black, respectively). Moreover, the z-axis could be made to show the recovery time while the size of the data point could be used to indicate the percent load impacted. It is understood that many other parameters/dimensions may be incorporated as makes sense and/or is useful.

e. Mitigation of Sag/Swell/Transient Impact Using Dynamic Tolerance Curves

As noted above, electrical systems are typically sensitive to voltage events in varying degrees. For some energy consumers, voltage events may just be a nuisance (no significant impact); for other energy consumers, any voltage event may be catastrophic. As previously discussed, quantifying the impact of voltage events helps energy consumers determine the severity, prevalence, and influence of these events on their operation. If voltage events have an impact the energy consumer's operation, the next step should be identifying the problem so it can be reduced or eliminated altogether.

In embodiments, eliminating or reducing the impact of voltage sags/swells/transients (and momentary, temporary and instantaneous interruptions) for the various embodiments discussed throughout the disclosure, can be generally accomplished in three ways: 1) removing the source of the voltage events, 2) reducing the number or severity of voltage events produced by the source, or 3) minimizing the effects of the voltage events on impacted equipment. In some embodiments, it is substantially difficult to remove the source (or sources) of voltage events because these same sources are usually an integral component or load within the facility's electrical infrastructure, process, and/or operation. Additionally, the voltage event's source may be located on the utility, and thus, hamper the ability to directly address a problem. If the voltage event's source is located inside the energy consumer's facility, it may be possible to minimize voltage events at the source by using different techniques or technologies (e.g., “soft-start” motors instead of “across the line” motor starting). In some embodiments, removing or replacing the source (or sources) of voltage events may cost-prohibitive and require an extensive redesign of an electrical system or subsystem. It is also possible to “desensitize” equipment against the effects of voltage events such as sags, swells, and transients. As always, there are economic trade-offs when considering the best approach to reduce or eliminate voltage issues. FIG. 21 is a generally recognized illustration showing the progression in cost to mitigate voltage events and other PQ-related issues, which tends to increase as the solution moves closer to the source. A thorough economic evaluation may include both the initial and total life cycle costs for a given solution. Furthermore, it may be very important to consider the response of any prospective solution to both internal and external sources of system voltage perturbations.

As an example, motors are an important electrical apparatus used in most processes. Standard (across the line) motor starts typically produce voltage sags due to the impedance between the source and motor and the motor's inrush current, which is typically 6-10 times the full-load current rating. Removing the motor from the process would most likely be impractical; however, reducing the voltage sag or minimizing its effects on adjacent equipment may be viable alternatives. A few example solutions may include using different motor technologies such as variable speed drives or to employ motor soft-start techniques to control or limit the inrush current (and thus, reduce or eliminate the voltage sag at start-up). Another example solution is to deploy one or more of several mitigative devices or equipment to reduce the voltage event's impact on sensitive equipment. Again, each electrical system is unique, so the cost to mitigate power quality disturbances may vary from location to location, system to system, and customer to customer.

This embodiment includes evaluating the ride-through characteristics of a multitude of mitigative devices against the dynamic tolerance-impact curves provided by each capable IED. The output of the evaluation may indicate the additional ride-through benefits of applying any particular mitigative device to any specific metering location. Moreover, a comparison of the economic, operational, and/or other benefits between two or more ride-through technologies or techniques for a specific system or sub-system may also be provided. In embodiments, in order to perform the evaluation, a managed collection (or library) of mitigative devices' ride-through characteristics may be assessed. The managed collection (or library) of mitigative devices may include (but not be limited to) characteristics and/or capabilities such as type, technology, magnitude vs. duration behavior, load constraints, typical applications, purchase costs, installation costs, operational costs, availability, purchase sources, dimensions/form factors, brands, and so forth for each known variety. In embodiments, the characteristics and capabilities described in the managed collection of mitigative devices will be considered (as required and as available) for application at every (or substantially every) discretely metered point (or sub-system) where data is obtainable and assessible. One or more ride-through characteristics curves (indicating magnitude vs. duration ride-through capabilities) for any or every mitigative device found in the managed collection (library) may be superimposed/overlaid on the dynamic tolerance curve for at least one or more discrete metering point(s). Alternatively, the evaluation may be provided through some other means accordingly. One or more characteristics and/or capabilities of the mitigative device(s) may be included in the evaluation against the dynamic tolerance curve based on factors such as those listed and available in the managed collection (or library). In embodiments, this evaluation may be alarm-driven, manually or automatically triggered, scheduled, or otherwise initiated.

The dynamic tolerance-impact curves provided by each capable IED for the electrical system's hierarchy (or portions of its hierarchy) may be evaluated against the ride-through characteristics of one or more mitigative devices. In embodiments, it may be more feasible, cost-effective, or otherwise beneficial to provide ride-through improvements as part of a system, sub-system process, and/or discrete location. Whereas it may be economical/practical/feasible to apply one type of ride-though mitigative solution for one device or one sub-system/zone, it may be more economical/practical/feasible to provide a different ride-through mitigative solution for another device or subsystem/zone within the electrical system. In short, the most economical/practical/feasible ride-through mitigative solution may be provided for the entire (or portion of the) electrical system based on the information available. In embodiments, other factors may be considered when determining ride-through improvements for one or more locations within an electrical system; however, this application emphasizes the importance of leveraging discretely established dynamic tolerance curves from one or more IEDs.

FIG. 22 illustrates the 2-D dynamic tolerance curve from FIG. 5. Again, this example shows a tolerance curve that has been customized and updated based on a single 50% voltage sag lasting 3 milliseconds and having a 20% load impact. An evaluation may be performed to ascertain the most economic/practical/feasible approach to improve the ride-through performance for this particular location in the electrical system. The managed collection (library) of mitigative devices may be assessed against suitable options and viable solutions. FIG. 23 shows the ride-through characteristics (magnitude vs. duration) of SagFighter® by Schneider Electric, which claims to meet SEMI F47, superimposed/overlaid on top of the updated dynamic tolerance curve. FIG. 24 provides the energy consumer with a graphical indication of SagFighter's ride-through benefits at this particular location in the electrical system (as indicated by the shaded area in FIG. 24, for example). Of course, the final mitigation device recommendation provided to the energy consumer may be dependent on more than the ride-through characteristic of the mitigative device (e.g. economical/practical/feasible/etc.). Additionally, this approach may be provided to multiple metered points across the electrical system or subsystems.

f. Determining Opportunity Costs for Ride-Through Mitigative Solutions Using Dynamic Tolerance Curves

As is known, opportunity cost refers to a benefit or gain that could have achieved, but was forgone in lieu of taking an alternative course of action. For example, a facility manager with a fixed budget may be able to invest funds to expand the facility OR to improve the reliability of the existing facility. The opportunity cost would be determined based on the economic benefit of the choice not taken by the facility manager.

In this embodiment of the disclosure, the “opportunity cost” is expanded to include other benefits such as production losses, material losses, recovery time, load impact, equipment losses, third-party losses, and/or any other loss that is quantifiable by some measure. Additionally, an “alternative course of action” may be the decision to take no action at all. A few benefits of taking no action include resource savings, monetary savings, time savings, reduced operational impact, deferral, and so forth. That is to say, decision-makers often consider the benefits of taking no action greater than the benefits of taking specific action(s).

The decision not to take an action is often based on the lack of information related to a given problem. For example, if someone cannot quantify the benefits of taking a particular action, they are less likely to take that action (which may be the wrong decision). Conversely, if someone is able to quantify the benefits of taking a particular action, they are more likely to make the right decision (whether to take action or not take action). Moreover, having quality information available provides the tools to produce other economic assessments such as cost/benefit analyses and risk/reward ratios.

This embodiment of this disclosure may continuously (or semi-continuously) evaluate the impact of voltage events (sags/swells/transients) against the ride-through tolerance characteristics of one or more mitigative devices, apparatuses and/or equipment. The evaluation may consider historical data to continuously track voltage events, associated discrete and combined system impact (e.g., as a relative value, absolute value, demand, energy, or other quantifiable energy-related characteristic), sub-system and/or system perspective, hierarchical impact from two or more devices, zones, cross-zones, or combination thereof. Information taken from the evaluation may be used to provide feedback and metrics regarding the operational repercussions that could have been avoided if one or more mitigative devices, apparatuses, and/or equipment would have been installed at a location (or locations).

For example, FIG. 25 provides a 2-D graph that illustrates events (and any associated impacts) that could have been avoided (green circles) if the decision had been made to install SagFighter® prior to the respective voltage event. FIG. 26 illustrates a similar graph as shown in FIG. 25, but also includes the estimated recovery time that could have been avoided had mitigative solutions been implemented prior to the voltage events. Metrics associated with these potentially avoided events (e.g., relative impact (%), absolute impact (W, kW, etc.), recovery time per event, accumulated recovery time, downtime, losses, other quantifiable parameters, etc.) may also be provided to an energy consumer to help justify investments to resolve voltage sag issues. The energy consumer (or systems and methods of the disclosure herein) could also choose what level of mitigation would be justifiable by comparing differing mitigation techniques to the historical tolerance curve data (i.e., the point of diminishing region of interest (ROI)). Metrics may be listed per event or accumulated, provided in a table or graphed, analyzed as a discrete point or from two or more devices (i.e., a system level perspective), or otherwise manipulated to indicate and/or quantify the impact and/or opportunity cost for not installing voltage event mitigation. The same information could be displayed a 3-D orthographic perspective of a tolerance-impact curve incorporating at least three parameters such as: 1) percent of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percent load impacted (or recovery time in days, hours or minutes) on the z-axis. While the y-axis is presented in units of percent of nominal voltage in the illustrated embodiment, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in the illustrated embodiment, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). Other parameters, characteristics, metadata, and/or mitigative apparatus may similarly be incorporated into a graph and/or table.

g. Verifying the Effectiveness of Mitigation Techniques Using Dynamic Tolerance Curves

Re-evaluating or reassessing the decision to invest in a facility's infrastructure is often overlooked, presumed, or merely based on speculation and supposition. In most cases the benefits of installing mitigative technologies are just assumed, but never quantified. Measurement and Verification (M&V) processes focus on quantifying energy savings and conservation; however, steps to improve reliability and power quality are not considered.

Embodiments of this disclosure periodically or continuously provide the following example benefits:

-   -   Allocate risks between contractors and their customer (e.g., for         performance contracts),     -   Accurately assess voltage events to quantify savings (in impact,         recovery time, uptime, losses, or other economic factors),     -   Reduce voltage quality uncertainties to reasonable levels,     -   Aid in monitoring equipment performance,     -   Identify additional monitoring and/or mitigation opportunities,     -   Reduce impact to targeted equipment, and     -   Improve operations and maintenance.

The dynamic voltage-impact tolerance curve provides a baseline of voltage events at each discretely metered point that captures impacted or potentially impacted processes, operations or facilities. Post-installation evaluations may be performed using data taken from the areas predicted to experience the benefits. In embodiments, these post-installation evaluations compare “before vs. after” to quantify the real benefits of installing the mitigative equipment. Determined quantities may include reduced event impact, recovery time, operational costs, maintenance costs, or any other operational or economic variable. An exemplary equation to determine the calculated savings due to installing mitigative equipment may be: Savings=(baseline costs−reduced downtime costs)±Adjustments where “reduced downtime costs” may include all or some combination of the following:

-   -   Reduced production losses,     -   Reduced restart losses,     -   Reduced product/material losses,     -   Reduced equipment losses,     -   Reduced 3^(rd) party costs, and     -   . . . and/or some other loss reduction.

Installation costs for the mitigative equipment may need to be considered, likely as an “adjustment,” in some embodiments.

FIG. 27 illustrates an example 2-D dynamic voltage tolerance curve according to the disclosure where the blue threshold lines (-) represent the ride-through baseline thresholds at a discretely metered point and the pink line (-) represents the predicted improvement to the voltage event ride-through thresholds by installing a certain type of mitigation equipment. The green circles in FIG. 27 indicate the voltage events (and consequently, the recovery time) expected to be avoided by installing the mitigation equipment. FIG. 28 illustrates an example 2-D dynamic voltage tolerance curve according to the disclosure showing the actual voltage events and recovery time avoided due to the installation of the mitigation equipment. The orange line (-) illustrates the actual improvement to the voltage ride-through thresholds by installing the mitigation equipment. In this example, the mitigation equipment surpassed its expectations by avoiding three additional voltage events and 22 hours (42 actual events−20 predicted events) of additional recovery time.

Each electrical system is unique and will perform differently to some degree. Embodiments of this disclosure use empirical data to characterize the actual performance of the mitigation equipment. For example, the actual thresholds for voltage ride-through (-) may perform better than expected as shown in FIG. 28 because the downstream load on the mitigation equipment was/is less than expected. This allows the mitigation device to ride-through longer than anticipated. Conversely, exceeding the mitigation equipment's load rating would likely result in a worse-than-expected performance. As the mitigation equipment's load continues to be increased beyond its rating, the voltage ride-through thresholds (-) will approach the original voltage ride-through threshold (-) or possibly be even more severe.

A 3-D dynamic tolerance curve similar to the one shown in FIG. 15 may be produced to better demonstrate the effect of mitigation on other parameters such as load impact, recovery time, economic factors, etc. In this case, at least three dimensions would be used to characterize the electrical system at the point of the IED's installation. A 3-D evaluation would provide an even better intuitive understanding of a mitigation equipment's historical, present and/or future performance. It would also make the selection of mitigation equipment for future applications less complicated and more cost-effective.

Metrics associated with the expected (based on historical data) and actually avoided events (e.g., relative impact (%), absolute impact (W, kW, etc.), reduced losses, other quantifiable parameters, etc.) may be provided to an energy consumer to help justify the original or additional investments to resolve voltage sag issues. Metrics may be listed per event or accumulated, provided in a table or graphed, analyzed as a discrete point or from two or more devices (i.e., a system level perspective), or otherwise manipulated to indicate and/or quantify the benefits and/or costs per avoided minute of impact due to the installation of voltage event mitigation. The same information could be displayed as a 3-D orthographic perspective of a tolerance-impact curve incorporating at least three parameters such as: 1) percent of nominal voltage on the y-axis, 2) duration in cycles and seconds on the x-axis, and 3) percent load impacted (or recovery time) on the z-axis. While the y-axis is presented in units of percent of nominal voltage in the illustrated embodiment, it is understood that the y-axis units may also be in absolute units (e.g., real values such as voltage), or substantially any other descriptor of the y-axis parameter's magnitude. Additionally, while the x-axis is logarithmic in the illustrated embodiment, it is understood that the x-axis does not have to be logarithmic (for example, it can be linear as well). Other parameters, characteristics, metadata, and/or mitigative apparatus could similarly be incorporated into a graph and/or table, for example.

II. Using Non-PQ IEDs to Help Quantify Voltage Event Impact

The ability to quantify the impact of a voltage event may be derived from measured changes in energy, current, or power flows (i.e., consumption). An IED may be used to provide these measurements. The measurements may be acquired in real-time (e.g., via direct MODBUS reads), historically (e.g., logged data), or by some other means.

Power monitoring systems often incorporate a diverse array of IEDs that are installed throughout the energy consumer's electrical system. These IEDs may have different levels of capabilities and feature sets; some more and some less. For example, energy consumers often install high-end (many/most capabilities) IEDs at the location where electrical energy enters their premises (M₁ in FIG. 29). This is done to acquire the broadest understanding possible of the electrical signals' quality and quantity as received from the source (typically, the utility). Because the budget for metering is usually fixed and the energy consumer often wants to meter as broadly as possible across their electrical system, conventional wisdom stipulates using IEDs with progressively lower capabilities as the installed meter points get closer to the loads (see FIG. 29, for example). In short, the majority of facilities incorporate many more low/mid-range IEDs than high-end IEDs.

“High-end” metering platforms (and some “mid-range” metering platforms) are more expensive and generally capable of capturing PQ phenomena including high-speed voltage events. “Low-end” metering platforms are less expensive and generally have reduced processor bandwidth, sample rates, memory, and/or other capabilities as compared to high-end IEDs. The emphasis of low-end IEDs, including energy measurements taken in most breakers, UPSs, VSDs, etc., is typically energy consumption or other energy-related functions, and perhaps some very basic PQ phenomena (e.g., steady-state quantities such as imbalance, overvoltage, undervoltage, etc.).

This feature leverages (i.e., interrelates, correlates, aligns, etc.) one or more voltage event indicators, statistical derivations and/or other information from a high-end IED with one or more similar and/or disparate measured parameters from a low-end IED with the goal of quantifying the impact, recovery time, or other event characteristic at the low-end IED. One exemplary method to do this is by using the voltage event timestamp (indicator of the moment a voltage event occurs) from the high-end IED as a reference point for evaluating a measurable parameter corresponding with the same timestamp at a low-end that does not inherently have the capability to capture voltage events. Data evaluated at both the high-end, mid-range, and low-end IEDs may include (but not be limited to) the event magnitude, duration, phase or line values, energy, power, current, sequential components, imbalance, timestamp, pre/during/post-event changes, any other measured or calculated electrical parameter, metadata, meter characteristics, and so forth. Again, the measurements may be acquired in real-time (e.g., via direct MODBUS reads), historically (e.g., logged data), or by some other means.

Another example way to leverage non-PQ IEDs is to extend the use of event alarms (including voltage events) derived from high-end IEDs. For example, when a high-end IED detects a voltage event, coincident data from low-end IEDs is analyzed to ascertain the impact, recovery time, or other event characteristic and/or parameter. If analysis of data from the low-end IED indicates some level of impact did occur, a voltage event alarm, impact alarm, and/or other alarm type may be generated by the system performing the analysis of the coincident data. The alarm information may include any relevant parameter and/or information as measured by the low-end IED, high-end IED, metadata, meter characteristics, load impact, recovery time, which one or more high-end IEDs triggered the low-end IED alarm, and so forth.

FIGS. 29 and 30 illustrate a relatively simple example of this embodiment of the disclosure. At time to, a high-end IED installed a respective metering point or location M₁ indicates the beginning of a voltage event. The pre-event load is measured, and the recovery time clock begins for the IED installed at the metering location M₁. Other relevant data, metrics and/or statistically derived information may also be measured or calculated as required. Simultaneously, the software (on-site and/or cloud-based) and/or hardware managing the metering system evaluates the other connected IEDs to determine whether any other IED installed at another respective metering point or location (e.g., M₂, M₃, M₄, M₅, M₆, M₇, M₈, M₉, M₁₀) concurrently experienced an impactful event. In this example, the IED installed at metering location M₇ is found to have experienced a coincident impactful event (the other devices are ignored in this example for the sake of simplicity). The pre-event load is determined from M₇ and the recovery time clock begins for M₇ using the voltage event's timestamp as a reference. With the IEDs installed at metering locations M₁ and M₇ identified as impacted by the voltage event, the impact is quantified based on pre/during/post-event electrical parameters (e.g., power, current, energy, voltage, etc.) with to derived from the IED installed at metering location M₁ and used as a reference point for both devices M₁ and M₇. The IED installed at metering location M₇ is located downstream from the IED installed at metering location M₁ and experiences a more significant relative impact (i.e., bigger percentage of its pre-event load) due to system impedance and uniquely affected loads. The recovery time counters for the IEDs installed at metering locations M₁ and M₇ are independent of each other, as will be the case for all IEDs. In this example, the IED installed at metering location M₇ experiences approximately the same recovery time as the IED installed at metering location M₁ (i.e., t_(M1r)≈t_(M7r)); however, that may not always be the case since recovery time may be unique at each metered location.

In embodiments, virtual metering may also be used to identify an impact of a voltage event on unmetered loads. For example, there are two electrical paths downstream from the IED installed at metering location M₁ in FIG. 30A. The electrical path on the right is metered through a physical IED (e.g., installed at metering location M₂); however, the electrical path on the left is not directly metered by a physical IED. If the load data measured by the IEDs installed at metering locations M₁ and M₂ are measured synchronously or pseudo-synchronously, it is possible to determine (within the accuracy and synchronization constraints of the IEDs installed at metering locations M₁ and M₂) the load flowing through the unmetered path, V₁, by the following equation: V₁=M₁−M₂. V₁ represents a location of a “virtual meter” or a “virtual IED” in the electrical system, and it signifies the difference between the IEDs installed metering locations M₁ and M₂ for any synchronous (or pseudo-synchronous) load measurement.

For this example, consider a fault that occurs downstream from the IED installed at metering location M₁ and upstream from the virtual meter located at metering location V₁ in FIG. 30A. Using the concept of virtual metering as described above, a load change is determined to have occurred in the unmetered path. Because the load data through the unmetered path may be derived from the IEDs installed at metering locations M₁ and M₂, it is possible to calculate the load impact to the unmetered path due to the fault. In this example, other important parameters related to this embodiment of the disclosure may also be derived from virtual meters including recovery time, economic impact, and so forth.

In one embodiment, the data sample rate (e.g., power, current, energy, voltage, or other electrical parameters) for IEDs installed at metering locations M₁, M₇, and/or any other IEDs may be dependently or independently increased as required after a voltage event has been indicated in order to provide more accurate results (e.g., recovery time). Data may be shown in a tabular format, graphically in 2-D or 3-D, color coded, shaded, as timelines from discrete IEDs, zonally, hierarchically, or as a system (aggregated) view, linearly or logarithmically, or in any other structure or method considered relevant and/or useful. The output of this embodiment may be via report, text, email, audibly, screen/display, or by some other interactive means.

Referring to FIGS. 30B-30I, several example figures are provided to further illustrate the concept of virtual metering in accordance with embodiments of this disclosure. As discussed above, an electrical system typically includes one or more metering points or locations. As also discussed above, one or more IEDs (or other meters, such as virtual meters) may be installed or located (temporarily or permanently) at the metering locations, for example, to measure, protect, and/or control a load or loads in the electrical system.

Referring to FIG. 30B, an example electrical system including a plurality of metering locations (here, M₁, M₂, M₃) is shown. In the illustrated embodiment, at least one first IED is installed at the first metering location M₁, at least one second IED is installed at the second metering location M₂, and at least one third IED is installed at the third metering location M₃. The at least one first IED is a so-called “parent device(s),” and the at least one second IED and the at least one third IED are so-called “child devices.” In the example embodiment shown, the at least one second IED and the at least one third IED are children of the at least one first IED (and, thus siblings with each other), for example, due to the at least one second IED and the at least one third IED both being installed at respective metering locations M₂, M₃ in the electrical system that “branch” from a common point (here, connection 1) associated with the metering location M₁ at which the at least one first IED is installed. Connection 1 is the physical point in the electrical system where the energy flow (as measured at M₁ by the at least one first IED) diverges to provide energy to the left and right electrical system branches (as measured at M₂ and M₃ by the at least one second IED and the at least one third IED, respectively).

The electrical system shown in FIG. 30B is an example of a “completely metered” system, where all branch circuits are monitored by a physical IED (here, the at least one first IED, the at least one second IED, and the at least one third IED). In accordance with various aspects of this disclosure, dynamic tolerance curves can be independently developed for each discrete metered location (M₁, M₂, M₃) without any dependence on external input(s) from other IEDs. For example, electrical measurement data from energy-related signals captured by the at least one first IED installed at the first metering location M₁ may be used to generate a unique dynamic tolerance curve for the metering location M₁ (e.g., as shown in FIG. 30C) without any input (or data) from the at least one second IED or the at least one third IED. Additionally, electrical measurement data from energy-related signals captured by the at least one second IED installed at the second metering location M₂ may be used to generate a unique dynamic tolerance curve for the metering location M₂ (e.g., as shown in FIG. 30D) without any input (or data) from the at least one first IED or the at least one third IED. Further, electrical measurement data from energy-related signals captured by the at least one third IED installed at the third metering location M₃ may be used to generate a unique dynamic tolerance curve for the metering location M₃ (e.g., as shown in FIG. 30E) without any input (or data) from the at least one first IED or the at least one second IED.

Referring to FIG. 30F, in which like elements of FIG. 30B are shown having like reference designations, another example electrical system is shown. Similar to the electrical system shown in FIG. 30B, the electrical system shown in FIG. 30F includes a plurality of metering locations (here, M₁, M₂, V₁). Also, similar to the electrical system shown in FIG. 30B, the electrical system shown in FIG. 30F includes at least one metering device installed or located at each of the metering locations (M₁, M₂). Here, however, unlike the electrical system shown in FIG. 30B, the electrical system shown in FIG. 30F includes a virtual meter (V₁) accordance with embodiments of this disclosure.

In the illustrated embodiment, at least one first IED is installed at a first “physical” metering location M₁, at least one second IED is installed at a second “physical” metering location M₂, and at least one virtual meter is derived (or located) at a “virtual” (non-physical) metering location V₁. The at least one first IED is a so-called “parent device” and the at least one second IED and the at least one virtual meter are so-called “child devices”. In the example embodiment shown, the at least one second IED and the at least one virtual meter are children of the at least one first IED (and, thus considered to be siblings with each other). In the illustrated embodiment, the at least one second IED and the at least one virtual meter are installed and derived, respectively, at respective metering locations M₂, V₁ in the electrical system that “branch” from a common point (here, connection 1) associated with the metering location M₁ at which the at least one first IED is installed. Connection 1 is the physical point in the electrical system where the energy flow (as measured at M₁ by the at least one first IED) diverges to provide energy to the left and right branches (as measured at M₂ by the at least one second IED, and as calculated for V₁ by the at least one virtual meter).

In accordance with embodiments of this disclosure, electrical measurement data associated with the virtual metering location V₁ may be created/derived by calculating the difference between the synchronous (or pseudo-synchronous) data from the at least one first IED (here, a parent device) installed at the first metering location M₁ and the at least one second IED (here, a child device) installed at the second metering location M₂. For example, electrical measurement data associated with the virtual metering location V₁ may be derived by calculating the difference between electrical measurement data from energy-related signals captured by the at least one first IED and electrical measurement data from energy-related signals captured by the at least one second IED, at a specific point in time (e.g., V₁=M₁−M₂, for synchronous or pseudo-synchronous data). It is understood that virtual meters (e.g., the at least one virtual meter located at virtual metering location V₁) may include data from one or more unmetered branch circuits, which are inherently aggregated into a single representative circuit.

The electrical system shown in FIG. 30F is an example of a “partially metered” system, where only a subset of the total circuits is monitored by physical IEDs (here, the at least one first IED and the at least one second IED). In accordance with various aspects of this disclosure, dynamic tolerance curves can be independently developed for each physically metered point (M₁, M₂) without any dependence on external input(s) from other IEDs. Additionally, in accordance with various aspects of this disclosure, the dynamic tolerance curve for a virtually metered point (V₁) is derived using select synchronous (or pseudo-synchronous) and complementary data (e.g., power, energy, voltage, current, harmonics, etc.) from physical IEDs (here, the at least one first IED, and the at least one second IED), and is dependent (sometimes, completely dependent) on these devices (here, the at least one first IED, and the at least one second IED). For example, returning briefly to FIGS. 30C-30E, the dynamic tolerance curve for virtual metered point V₁ may be derived from the dynamic tolerance curve data for physical metered points M₁, M₂ (e.g., as shown in FIGS. 30C and 30D, respectively). Because of this dependency, it is understood that issues (e.g., accuracy, missing data, non-synchronous data, etc.) with the at least one first IED and the at least one second IED will be reflected in the resulting virtual meter data in the illustrated embodiment. In the illustrated embodiment, the dynamic tolerance curve for virtual metered point V₁ may be the same as (or similar to) the dynamic tolerance curve shown in FIG. 30E as an example.

Referring to FIG. 30G, a further example electrical system includes at least one virtual meter located at a “virtual” metering location V₁, at least one first IED installed at a first “physical” metering location M₁, and at least one second IED installed at a second “physical” metering location M₂. The at least one virtual meter is a so-called “parent device” or “virtual parent device,” and the at least one first IED and the at least one second meter are “child devices.” In the example embodiment shown, the at least one first IED and the at least one second IED are children of the at least one virtual meter (and, thus considered to be siblings with each other).

As illustrated, the at least one first IED and the at least one second IED are both installed (or located) at respective metering locations M₁, M₂ in the electrical system that “branch” from a common point (here, connection 1) associated with the virtual metering location V₁ at which the at least one virtual meter is derived (or located). Connection 1 is the physical point in the electrical system where the energy flow (as calculated at V₁) diverges to provide energy to the left and right branches (as measured at M₁ and M₂ by the at least one first IED and the at least one second IED, respectively).

In accordance with embodiments of this disclosure, electrical measurement data associated with the first metering location V₁ is created/derived through a slightly different approach than described above in connection with FIG. 30F, for example. In particular, the electrical measurement data associated with the first metering location V₁ may be determined by calculating the summation of synchronous (or pseudo-synchronous) data from the at least one first child IED installed at metering location M₁ and the at least one second child IED device installed at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous or pseudo-synchronous data).

The electrical system shown in FIG. 30G is an example of a “partially metered” system, where only a subset of the total circuits is monitored by physical IEDs. In accordance with various aspects of this disclosure, dynamic tolerance curves can be independently developed for each physically metered point (M₁, M₂) without any dependence on external input(s) from other IEDs. Additionally, in accordance with various aspects of this disclosure, the dynamic tolerance curve for the virtual parent meter (V₁) is derived using select complementary data (e.g., power, energy, voltage, current harmonics, etc.) from physical IEDs (M₁, M₂), and is completely dependent on these devices (M₁, M₂). Because of this dependency, it is understood that any issue (e.g., accuracy, missing data, non-synchronous data, etc.) with meters M₁ and M₂ will be reflected in virtual parent device V₁.

Referring to FIG. 30H, another example electrical system includes at least one first virtual meter located at a first “virtual” metering location V₁, at least one first IED installed at a first “physical” metering location M₁, and at least one second virtual meter installed at a second “virtual” metering location V₂. The at least one virtual meter is a “parent device” or a “virtual parent device”, and the at least one first IED and the at least one second virtual meter are “child devices”. In the example embodiment shown, the at least one first IED and the at least one second virtual meter are children of the at least one first virtual meter (and, thus considered to be siblings with each other).

As illustrated, the at least one first IED and the at least one second virtual meter are installed and derived, respectively, at respective metering locations M₁, V₂ in the electrical system that “branch” from a common point (here, connection 1) associated with the first virtual metering location V₁ at which the at least first one virtual meter is located (or derived). Connection 1 is the physical point in the electrical system where the energy flow (as calculated at V₁) diverges to provide energy to the left and right branches (as measured at M₁ by the at least one first IED, and as calculated at V₂).

In accordance with some embodiments of this disclosure, the electrical system shown in FIG. 30H is mathematically and probabilistically indeterminate because there are too many unknown values from necessary inputs. Assumptions may be made regarding the occurrence of power quality events (e.g., voltage events) on the virtual devices (V₁, V₂); however, the impact of the power quality events may impossible (or extremely hard) to define in this case. As is appreciated from discussions above and below, virtual metering data is derived from data taken from physical IEDs. In the embodiment shown in FIG. 30H, there are too few physical IEDs to derive the “virtual” data. FIG. 30H is shown to illustrate some constraints related to virtual IED derivations.

Referring to FIG. 30I, a further example electrical system includes at least four virtual meters (or IEDs) located (or derived) at respective “virtual” metering locations (V₁, V₂, V₃, V₄) in the electrical system, and at least five IEDs installed at respective “physical” metering locations (M₁, M₂, M₃, M₄, M₅) in the electrical system. In particular, the electrical system includes at least one first “parent” virtual meter located at a first “virtual” metering location V₁, at least one first “child” IED installed at a first “physical” metering location M₁, and at least one second “child” IED installed at a second “physical” metering location M₂ (with the at least one first IED at metering location Wand the at least one second IED at metering location M₂ being children of the at least one first virtual meter at metering location/position V₁). The electrical system also includes at least one third “child” IED installed at a third “physical” metering location M₃ and at least one second “child” virtual meter located at a second “virtual” metering location V₂ (with the at least one third IED at metering location M₃ and the at least one second virtual meter at metering location V₂ being children of the at least one first IED at metering location M₁).

The electrical system further includes at least one fourth “child” IED installed at a fourth “physical” metering location M₄ and at least one third “child” virtual meter located at a third “virtual” metering location V₃ (with the at least one fourth IED at metering location M₄ and the at least one third virtual meter at metering location V₃ being children of the at least one second virtual meter at metering location V₂). The electrical system also includes at least one fifth “child” IED installed at a fifth “physical” metering location M₅ and at least one fourth “child” virtual meter located at a fourth “virtual” metering location V₄ (with the at least one fifth IED at metering location M₅ and the at least one fourth virtual meter at metering location V₄ being children of the at least one third virtual meter at metering location V₃). As illustrated, there are essentially five layers in the metering hierarchy from the first virtual metering location V₁, to the fifth “physical” metering location M₅ and the fourth “virtual” metering location V₄.

The electrical system shown in FIG. 30I illustrates a partially metered system, where only a subset of the total circuits is monitored by physical devices/IEDs. In accordance with various aspects of this disclosure, dynamic tolerance curves can be independently developed for each physically metered location (M₁, M₂, M₃, M₄, M₅) without any dependence or interdependence on external input(s) from other IEDs. The dynamic tolerance curves for the virtual metering locations V₁, V₂, V₃, V₄ may be derived from complementary and synchronous (or pseudo-synchronous) data (e.g., power, energy, voltage, current, harmonics, etc.) as measured by physical IEDs installed at the discretely metered locations M₁, M₂, M₃, M₄, M₅. Additionally, electrical measurement data from energy-related signals captured by the at least one second IED installed at the second metering location M₂ may be used to generate a dynamic tolerance curve for the metering location M₂ without any input (or data) from the at least one first IED or the at least one third IED.

In particular, the electrical measurement data associated with the first virtual metering location V₁ may be determined (and used to help generate a dynamic tolerance curve for the first virtual metering location V₁) by calculating the summation of synchronous (or pseudo-synchronous) data from the at least one first child IED installed at metering location M₁ and the at least one second child IED device installed at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous or pseudo-synchronous data). Additionally, the electrical measurement data associated with the second metering location V₂ may be determined (and used to help generate a dynamic tolerance curve for the second virtual metering location V₂) by calculating the difference between synchronous (or pseudo-synchronous) data from the at least one first child IED installed at metering location M₁ and the at least one third child IED device installed at metering location M₃ (e.g., V₂=M₁−M₃, for synchronous or pseudo-synchronous data).

The electrical measurement data associated with the third virtual metering location V₃ may be determined (and used to help generate a dynamic tolerance curve for the third virtual metering location V₃) by first calculating the difference between synchronous (or pseudo-synchronous) data from the at least one first child IED installed at metering location M₁ and the at least one third child IED device installed at metering location M₃, and then calculating the difference between the first calculated difference and synchronous (or pseudo-synchronous) data from the at least one fourth child IED installed at metering location M₄ (e.g., V₃=M₁−M₃−M₄, for synchronous or pseudo-synchronous data).

Additionally, the electrical measurement data associated with the fourth virtual metering location V₄ may be determined (and used to help generate a dynamic tolerance curve for the fourth virtual metering location V₄) by first calculating the difference between synchronous (or pseudo-synchronous) data from the at least one first child IED installed at metering location M₁ and the at least one third child IED device installed at metering location M₃, and then calculating the difference between the synchronous (or pseudo-synchronous) data from the at least one fourth child IED installed at metering location M₄ and the at least one fifth child IED installed at metering location M₅. The difference between the first calculated difference and the calculated difference between the synchronous (or pseudo-synchronous) data from the at least one fourth child IED installed at metering location M₄ and the at least one fifth child IED installed at metering location M₅ may be used to determine the electrical measurement data associated with the fourth virtual metering location V₄ (e.g., V₄=M₁−M₃−M₄−M₅, for synchronous or pseudo-synchronous data).

As will be further appreciated from discussions below, using event triggers or alarms from one or more of the physical IEDs (M₁, M₂, M₃, M₄, M₅), it is possible to use pre-event and post-event data from the physical IEDs to develop dynamic tolerance curves, determine event impacts, quantify recovery times, and assess other associated costs at the virtual meters (and metering locations V₁, V₂, V₃, V₄). Again, validity of the derived information for the virtual meter (V₁, V₂, V₃, V₄) is dependent on the veracity, accuracy, synchronicity, and availability of data from the physical IEDs (M₁, M₂, M₃, M₄, M₅). In this particular case, there are many interdependencies used to derive data for the virtual meters (and metering locations V₁, V₂, V₃, V₄), so it is understood that some deficiency may be experienced for one or more derivations.

It is understood that the above-described examples for determining, deriving, and/or generating dynamic tolerance curves for virtual meters in an electrical system may also apply to aggregation of zones and systems. In spirit of the concepts describing “operational impact,” “recovery time,” “recovery energy costs,” and so forth, it is understood that aggregation may only make sense when it is 1) directly useful to the customer/energy consumer, 2) and/or useful to be leveraged for additional customer and/or business-centered benefits (present or future). That is why the best approach to aggregation is typically to focus on the worst-case scenario (i.e., event impact, event recovery time, other associated event costs, etc.). If aggregation is performed and it does not reflect the customers experience in trying to resolve the event in question, then it is difficult to achieve any usefulness from the aggregation. In short, just because something is mathematically and/or statistically feasible does not necessarily make it useful.

III. Evaluating Load Impact and Recovery Time Using Hierarchy and Dynamic Tolerance Curve Data

In embodiments, when a load impacting voltage event occurs, it is important for the energy consumer (or the systems and methods disclosed herein) to prioritize the “what, when, why, where, who, how/how much/how soon, etc.” of the response. More specifically: 1) what happened, 2) when did it happen, 3) why did it happen, 4) where did it happen, 5) who's responsible, 6) how do I resolve the issue, 7) how much is it going to cost, and 8) how soon can I get it resolved. Embodiments described herein assist energy consumers with answering these questions.

Understanding and quantifying the impact of voltage (and/or other) events from a IED, zone, and/or system perspective is extremely important for energy consumers to understand their electrical system and facility's operation in its entirety, and to respond to electrical events accordingly. Because each load has unique operating characteristics, electrical characteristics and ratings, functions, and so forth, the impact of a voltage event may differ from one load to the next. This can result in unpredictable behavior, even with comparable loads connected to the same electrical system and located adjacent to each other. It is understood that some aspects of the embodiments described below may refer to or overlap with previously discussed ideas presented herein.

System (or hierarchical) perspectives show how an electrical system or metering system is interconnected. When a voltage event occurs, its impact is strongly influenced by the system impedance and sensitivity of a given load. For example, FIG. 31 illustrates a relatively simple fully-metered electrical system experiencing a voltage event (e.g., due to a fault). In general, the system impedance will dictate the magnitude of the fault, protective devices will dictate the duration of the fault (clearing time), and location of the fault will be an important factor in the scope of the fault's impact to the electrical system. In FIG. 31, its possible (even likely) the shaded area will experience a significant voltage sag followed by an interruption (due to the operation of protective device(s)). In embodiments, the duration of the event's impact will be from the onset time of the fault until the system is again operating normally (note: this example states a recovery time of 8 hours). The unshaded area of the electrical system in FIG. 31 may also experience a voltage event due to the fault; however, the recovery time for the unshaded area will likely be briefer than the shaded area.

In embodiments, both the shaded and unshaded areas of the electrical system shown in FIG. 31 may be impacted by the fault; however, both may exhibit different recovery time durations. If the processes served by both the shaded and unshaded areas are critical to the facility's operation, then the system recovery time will be equal to the greater of the two recovery times.

In embodiments, it is important to identify and prioritize IEDs, zones, and/or systems. Zones may be determined within the electrical system hierarchy based on: protection schemes (e.g., each breaker protects a zone, etc.), separately derived sources (e.g., transformers, generators, etc.), processes or sub-systems, load types, sub-billing groups or tenants, network communications schemes (e.g., IP addresses, etc.), or any other logical classification. Each zone is a subset of the metering system's hierarchy, and each zone may be prioritized by type and each zone may be assigned more than one priority if applicable (e.g., high priority load type with low priority process). For example, if a protective device also acts as a IED and is incorporated into the metering system, it and the devices below it could be considered a zone. If the protective devices are layered in a coordinated scheme, the zones would be similarly layered to correspond with the protective devices. In FIG. 32, another method to automatically determine zones involves leveraging hierarchical context to evaluate voltage, current, and/or power data (other parameters may also be used as necessary) to identify transformer locations. FIG. 32 indicates three zones: utility source, transformer 1, and transformer 2. FIG. 33 is an exemplary illustration of an energy consumer's custom zone configuration.

Once the zones are established, prioritizing each zone will help the energy consumer better respond to voltage events (or any other event) and their impact. While there are techniques to automatically prioritize zones (e.g., largest to smallest load, load types, recovery times, etc.), the most prudent approach would be for the energy consumer to rank the priorities of each zone. It is certainly feasible (and expected) for two or more zones to have an equal ranking in priority. Once zone priorities are established, it is then possible to analyze the load impact and recovery time for voltage events from a zonal perspective. Again, all of this may be automated using the techniques described above for establishing zones, prioritizing based on the historical effects of voltage events within the electrical system, and providing the energy consumer with analyses summaries based on these classifications.

Zones are also useful for identifying practical and economical approaches to mitigate voltage events (or other PQ issues). Because mitigation solutions can range from system-wide to targeted schemes, it is beneficial to evaluate mitigation opportunities the same way. As shown in FIG. 21 above, for example, mitigation solutions for voltage events become more expensive as the proposed solution moves closer to the electrical main switchgear.

In embodiments, evaluating zones to identify mitigation opportunities of voltage events can produce a more balanced, economical solution. For example, one zone may be more susceptible to voltage events (e.g., perhaps due to a local motor starting) than another zone. It may be possible to provide electrical service to sensitive loads from another zone. Alternatively, it may be prudent to move the cause of the voltage events (e.g., the local motor) to another service point in another zone.

A further example benefit of evaluating zones is the ability to prioritize capital expenditure (CAPEX) investments for voltage event mitigation based on the prioritization of each respective zone. Assuming the zones have been properly prioritized/ranked, important metrics such as percent load impacted (relative), total load impacted (absolute), worst case severity, recovery time, etc. may be aggregated over time to indicate the best solution and location for mitigative equipment. Using aggregated zonal voltage tolerance data from IEDs within the zone may provide a “best-fit” solution for the entire zone or locate a targeted solution for one or more loads within a zone.

IV. Alarm Management of IEDs Using Dynamic Tolerance Curves and Associated Impact Data

As discussed above, each location within an electrical system/network generally has unique voltage event tolerance characteristics. Dynamically (continuously) generating the distinct voltage event tolerance characteristics for one or more metered points in the electrical system provides many benefits including a better understanding of an electrical system's behavior at the metered point, suitable and economical techniques for mitigating voltage anomalies, verification that installed mitigation equipment meets its design criteria, leveraging non-PQ IEDs to help characterize voltage event tolerances, and so forth.

Another example advantage of characterizing a IED point's voltage event tolerance is to customize alarms at the IED's point of installation. Using dynamic voltage event characterization to manage alarms provides several benefits including ensuring 1) relevant events are captured, 2) excessive alarms are prevented (better “alarm validity”), 3) appropriate alarms are configured, and 4) important alarms are prioritized.

Existing approaches to alarm configuration and management often include:

-   -   Manual configuration by energy consumer based on standards,         recommendations, or guessing.     -   Some form of setpoint learning that necessitated a configuration         “learning period” to determine what was normal. Unfortunately,         if an event occurred during the learning period, it would be         considered normal behavior unless the energy consumer caught it         and omitted the data point.     -   “Capture Everything” approach that requires the energy consumer         to apply filters to distinguish which alarms are important and         which are not.

In short, the energy consumer (who may not be an expert) could be required to actively discriminate which event alarms/thresholds are important, either before or after the event alarms are captured in a “live system.”

Currently, IED voltage event alarms have two important thresholds that are typically configured: 1) magnitude, and 2) duration (sometimes referred to as alarm hysteresis). Equipment/loads are designed to operate at a given optimal voltage magnitude (i.e., rated voltage) bounded by an acceptable range of voltage magnitudes. Additionally, it may be possible for a load to operate outside the acceptable voltage range, but only for short periods of time (i.e., duration).

For example, a power supply may have a rated voltage magnitude of 120 volts rms±10% (i.e., ±12 volts rms). Therefore, the power supply manufacturer is specifying the power supply should not be operated continuously outside the range of 108-132 volts rms. More precisely, the manufacturer is making no promises regarding the power supply's performance or susceptibility to damage outside their prescribed voltage range. Less evident is how the power supply performs during momentary (or longer) voltage excursions/events outside the prescribed voltage range. Power supplies may provide some voltage ride-though due to their inherent ability to store energy. The length of voltage ride-through depends on a number of factors, primarily the amount/quantity of load connected to the power supply during the voltage excursion/event. The greater the load on the power supply, the shorter the power supply's ability to ride-though the voltage excursion/event. In summary, this substantiates the two parameters (voltage magnitude and duration during the voltage event), which also happen to be the same two parameters exemplified in basic voltage tolerance curves. It further validates load as an additional parameter that may be considered where a voltage event's impact and IED alarm thresholds are concerned.

In embodiments of this disclosure, a IED device's voltage magnitude alarm threshold may be initially configured with a reasonable setpoint value (e.g., the load's rated voltage±5%). The corresponding duration threshold may be initially configured to zero seconds (highest duration sensitivity). Alternatively, the IED device's voltage magnitude alarm threshold may be configured for ANY voltage excursion above or below the load's rated voltage (highest magnitude sensitivity). Again, the corresponding duration threshold (alarm hysteresis) may be initially configured to zero seconds (highest sensitivity).

As the metered voltage deviates beyond the voltage alarm threshold (regardless of its configured setpoint), the IED device may alarm on a voltage disturbance event. The IED may capture characteristics related to the voltage event such as voltage magnitude, timestamp, event duration, relevant pre/during/post-event electrical parameters and characteristics, waveform and waveform characteristics, and/or any other monitoring system indication or parameter the IED is capable of capturing (e.g., I/O status positions, relevant time stamps, coincident data from other IEDs, etc.).

Voltage events may be evaluated to determine/verify whether a meaningful discrepancy exists between a pre-event electrical parameter's value (e.g., load, energy, phase imbalance, current, etc.) and its corresponding post-event value. If a discrepancy does not exist (pre-event vs. post-event), the voltage event may be considered to be “non-impactful” meaning there is no indication the energy consumer's operation and/or equipment was functionally affected by the voltage event. The voltage event data may still be retained in memory; however, it may be classified as non-impactful to the energy consumer's operation at the point where the IED captured the voltage event. The existing voltage alarm magnitude and duration threshold setpoints may then reconfigure to the magnitude and duration of the non-impactful event (i.e., reconfigured to less sensitive setpoints). Ultimately, in embodiments the more severe voltage event that does not indicate any operational and/or equipment functional impact at the IED point will become the new voltage magnitude and duration threshold for the voltage event alarms for that respective IED.

If a pre-event vs. post-event discrepancy does exist, the voltage event may be considered to be “impactful” meaning there is at least one indication the energy consumer's operation and/or equipment was functionally affected by the voltage event. The voltage event data may be retained in memory, including all measured/calculated data and metrics related to the impactful event (e.g., % impacted, absolute impact, voltage magnitude, event duration, etc.). Moreover, additional relevant data associated with the voltage event may be appended to the voltage event data record/file at a later time (e.g., calculated recovery time from event, additional voltage event information from other IEDs, determined event source location, metadata, IED data, other electrical parameters, updated historical norms, statistical analysis, etc.). Because the voltage event is determined to be “impactful,” the voltage alarm magnitude and duration threshold setpoints are left unchanged to ensure less severe, yet still impactful, events continue to be captured by the IED at its respective installation point within the electrical system.

In embodiments, the final result of this process is the discrete IED device produces a custom voltage alarm template at the point of installation that indicates voltage events (and their respective characteristics) producing impactful events and/or differentiates impactful voltage events from non-impactful voltage events. As more voltage events occur, the custom voltage alarm template more accurately represents the true voltage event sensitivity at the IED's point of installation. In embodiments, it is possible to capture any (or substantially any) voltage event that exceeds any standardized or custom threshold; however, the energy consumers may choose to prioritize impactful events as a distinctive category of alarms/indicators. This could be used, for example, to minimize the inundation of superfluous voltage alarms in the energy consumer's monitoring system by annunciating only prioritized alarms considered to indicate an impactful had occurred.

As indicated above in connection with other embodiments of this disclosure, the tailored voltage tolerance curve built for customized voltage event alarm annunciation could also be used to recommend mitigation equipment to improve ride-through characteristics at the IED's point of installation. Should the energy consumer install mitigation equipment, a manual or automatic indication can be provided/detected by the system so a new version of the voltage tolerance template can be created based on the system modification (e.g., mitigation equipment installation). In embodiments, a practical approach may be a manual indication of supplemental mitigation equipment being added to the system; however, an automatic indication could also be provided based on “uncharacteristic changes” in the electrical system's response to voltage events at the point of the IED's installation, for example. These “uncharacteristic changes” could be established, for example, by statistically evaluating (e.g., via analytics algorithms) one or more electrical parameters (i.e., voltage, current, impedance, load, waveform distortion, and so forth). In embodiments, they may also be identified by any sudden change in voltage event ride through at the point of the IED's installation. A query may be made of the energy consumer or electrical system manager to validate any additions, eliminations or changes to the electrical network. Feedback from the energy consumer could be used to better refine any statistical evaluations (e.g., analytics algorithms) related to voltage events (or other metering features). Historical information (including customized voltage tolerance curves) would be retained for numerous assessments such as verification of the effectiveness of mitigation techniques, impact of new equipment installation to voltage ride-through characteristics, and so forth.

As part of this embodiment, more than two event parameters may be used to configure thresholds to trigger alarms for voltage events. In the description above, the magnitude of voltage deviation and the duration of the voltage event are used configure and trigger voltage event alarms. In embodiments, it is also possible to include more dimensions such as load impact and/or recovery time to configure voltage event alarms. Just as it is possible to set voltage event setpoint thresholds to alarms only when any load is impacted, it is also possible to configure voltage event setpoint thresholds to allow some level of impact to the load. Through load identification, either manually or automatically (based on electrical parameter recognition), it is possible to alarm when only certain types of loads experience an impact due to a voltage event. For example, some loads have certain signatures such as elevated levels of specific harmonic frequencies. In embodiments, it would be possible to trigger a voltage event alarm if those specific harmonic frequencies are no longer evident.

It is possible to use other parameters to customize the alarm templates. For example, the energy consumer may only be interested in voltage events with a recovery times greater than 5 minutes. Voltage event characteristics that typically produce recovery times shorter than 5 minutes could be filtered by using historical event data to configure the alarm templates accordingly. Moreover, energy consumers may only be interested in voltage events that generate monetary losses greater than $500. Again, voltage event characteristics that typically produce monetary losses less than $500 could be filtered using historical data to configure the alarm templates accordingly. As is apparent, any other useful parameter derived from voltage event characteristics may be similarly used to tailor and provide practical alarm configurations. Multiple parameters may also be concurrently used (e.g., recovery times>5 minutes AND monetary losses>$500) to provide more complex alarm schemes and templates, and so forth.

In embodiments, as more voltage events occur, additional voltage pre/during/post-event attributes and parameters are captured at both the discrete and system level and integrated into typical historical characterizations (historical norms). This additional characterization of voltage events can be used, for example, to estimate/predict the expected recovery time from both a discrete and system level. Additionally, recommendations can be made to energy consumers on how to achieve a faster recovery time based on historical event data regarding the effective sequencing to reenergize loads.

In embodiments, customer alarm prioritization can be performed (for voltage events or any other event type) based on the level of load measured at one or more discrete metering/IED points within the electrical system. When some indication is received from a metered/virtual/IED point that a load or loads have changed (or are operating atypically), voltage event alarm setpoint thresholds may be reevaluated and modified based on the level of load measured at one or more discrete (or based on the load's atypical operation). For example, it may be advantageous to null, silence or deprioritize the voltage event alarm when one or more IEDs indicate the measure load is low (indicating the facility is off-line). Conversely, raising the priority of the voltage event alarm would be prudent as one or more IEDs indicate additional loads being started.

As mentioned earlier in this section, in embodiments it is possible to use this feature to prioritize alarms (including voltage event alarms). The IED may be configured to capture data related to substantially any perceptible voltage variation from the nominal voltage (or load(s) rated voltage) at the point of installation, and take an action(s) including storing, processing, analyzing, displaying, controlling, aggregating, and so forth. Additionally, the same action(s) may be performed on substantially any alarms (including voltage event alarms) that exceed some pre-defined setpoint/threshold such as those defined by a dynamic voltage tolerance curve, standard(s), or other recommendations (as derived from any number or combination of electrical parameters, I/O, metadata, IED characteristics, etc.). In embodiments, any or all captured events (including voltage events) may then be analyzed to automatically prioritize the alarms at a discrete, zone and/or system level based on any number of parameters including: alarm type, alarm description, alarm time, alarm magnitude, affected phase(s), alarm duration, recovery time, waveform characteristics, load impact associated with an alarm, location, hierarchical aspects, metadata, IED characteristics, load type, customer type, economic aspects, relative importance to operation or load, and/or any other variable, parameter or combination thereof related to the event (including voltage events) and the energy consumer's operation. Prioritizing may be relevant for the inherent characteristics of discrete events or involve comparisons of more than one event (including voltage events), and may be performed as events originate, deferred to a later time, or dependent on the aforementioned parameters. In embodiments, prioritization may be interactive with the energy consumer, automated, or both with the goal being to facilitate the energy consumer's preferences.

In embodiments, parameters to be considered may include at least electrical data (from at least one phase), control data, time data, metadata, IED data, operational data, customer data, load data, configuration and installation data, energy consumer preferences, historical data, statistical and analytical data, economic data, material data, any derived/developed data, and so forth.

For example, FIG. 34 illustrates a relatively simple voltage tolerance curve for an IED with voltage alarm thresholds set at ±10% of the nominal voltage for events arbitrarily ranging from 1 usec to steady-state. In FIG. 35, a voltage sag event occurs on this IED that sags to 50% of the nominal voltage and lasts for 3 milliseconds in duration. Pre/during/post-event analysis of this event indicates no load was impacted. In embodiments, because no load was impacted, the alarm setpoint thresholds in the IED are reconfigured to indicate/prioritize the occurrence of a voltage event when (sometimes, only when) the magnitude and duration of a voltage event are more severe than the event described in FIG. 35. FIG. 36 illustrates changes made to the original voltage-tolerance curve. In short, voltage events occurring in the red area of the graph are expected to be non-impactful and voltage events occurring in the green area of the graph may or may not be impactful. In FIG. 37, another voltage event occurs and is captured by the same IED. In this second voltage event, a voltage interruption (to 0% of the nominal voltage) occurs and lasts for 1 millisecond in duration. Again, pre/during/post-event analysis of the second event indicates no load was impacted. And again, the alarm setpoint thresholds in the IED are reconfigured to indicate/prioritize the occurrence of a voltage event when (sometimes, only when) the magnitude and duration of the voltage event are more severe than the event described in FIG. 36. FIG. 38 illustrates changes made to the original voltage-tolerance curve.

In FIG. 39, a third voltage event occurs and is captured by the IED. In this third voltage event, the voltage sags to 30% of the nominal voltage and lasts for 2 milliseconds in duration. This time the pre/during/post-event analysis of the third event indicates 25% of the load was impacted (e.g., disconnected) at 30% of the nominal voltage. Subsequently, the alarms setpoint thresholds are left unchanged because of the 25% impact to the load (i.e., a load impact occurred where it was expected to occur). FIG. 40 illustrates the final settings of the voltage event alarm threshold after these three voltage events. Note that the third event is not shown on the graph because the purpose of this embodiment of the disclosure is to reconfigure/modify voltage event setpoint thresholds. The energy consumer may be notified of the third event occurrence, and the voltage event data, calculations, derivation and any analyses may be stored for future reference/benefits.

V. Evaluating and Quantifying Voltage Event Impact on Energy and Demand

Establishing the losses incurred due to voltage events is often complicated; however, embodiments of this disclosure provide an interesting metric (or metrics) to help quantify the energy and demand contribution to the total losses. When a voltage event occurs, facility processes and/or equipment may trip off-line. The activity of restarting processes and/or equipment consumes energy and can (in some cases) produce a peak demand for the facility. Although these costs are frequently overlooked, they may be considerable over time while contributing little to the actual production and profitability of a facility's operation. There may be ways to recoup some of these costs through insurance policy coverage, tax write-offs in some jurisdictions, and even peak demand “forgiveness” from the utility. Perhaps most importantly, quantifying the financial impact of voltage events to utility bills can provide incentives to mitigate the voltage events leading to these unexpected and potentially impactful losses.

When a voltage event occurs, the analyses described above may be performed to determine the level of impact to the load or operation. If no evidence is found of an impact on a load, process, and/or system, this aspect of this embodiment of the disclosure may be disregarded. If the voltage event is found to have impacted a load, process, and/or system, the pre/during/post-event analyses of electrical parameters are performed. The recovery time clock starts and this embodiment of the disclosure categorizes the energy consumption, demand, power factor, and any other parameter related to the utility billing structure as associated with the recovery time interval. Evaluation and analyses may be performed on these parameters to determine discrete, zonal and/or system metrics (including aggregation), comparisons to historical event metrics, incremental energy/demand/power factor costs and so forth. These metrics may be evaluated against local utility rate structures to calculate the total energy-related costs for recovery, discrete, zonal, and/or systems most susceptible and most costly during the recovery period for targeted mitigation, expectations based on historical voltage event data (e.g., number of events, recovery period of events, energy costs for events, etc.), opportunities to operationally/procedurally improve voltage event response time, and so forth.

In embodiments, the data and analyses collected before, during and/or after the recovery period may be filtered, truncated, summarized, etc. to help the energy consumer better understand the impact of the voltage event (or other event) on their electrical system, processes, operation, response time, procedures, costs, equipment, productivity or any other relevant aspect of their business's operation. It can also provide a useful summary (or detailed report) for discussions with utilities, management, engineering, maintenance, accounting/budgeting, or any other interested party.

VI. Disaggregation of Typical and Atypical Operational Data Using Recovery Time

It is important to recognize a facility's operation during a recovery period is often aberrant or atypical as compared to non-recovery times (i.e., normal operation). It is useful to identify, “tag” (i.e., denote), and/or differentiate aberrant or atypical operational data from normal operational data (i.e., non-recovery data) for performing calculations, metrics, analytics, statistical evaluations, and so forth. Metering/monitoring systems do not inherently differentiate aberrant operational data from normal operational data. Differentiating and tagging operational data as either aberrant (i.e., due to being in recovery mode) or normal provides several advantages including, but not limited to:

-   -   1. Analyses (such as the aforementioned) may assume operational         uniformity throughout all the data; however, it is useful to         disaggregate aberrant or atypical operational patterns from         normal operational patterns to better evaluate and understand         the significance of the data being analyzed. Data analysis is         improved by providing two different categories of operations;         normal and aberrant/abnormal/atypical. Each may be analyzed         automatically and independently to provide unique and/or more         precise information regarding each operational mode within a         facility or system. Differentiating normal operational data from         atypical operational data (i.e., due to a voltage event) further         bolsters decisions made based on the conclusions of analyses.     -   2. Differentiating normal and aberrant operational modes makes         it possible to provide discrete baseline information for each         operational mode. This provides the ability to better normalize         operation data because atypical data can be excluded from         analysis of system data. Additionally, aberrant operational         modes may be analyzed to help understand, quantify and         ultimately mitigate impacts associated with impactful voltage         events. In the case of event mitigation, data analysis of         aberrant operational periods will help identify possible more         effective and/or economical approaches to reducing the impact of         voltage events.     -   3. Losses incurred due to voltage events are generally difficult         to establish; however, evaluations of data tagged (i.e.,         partitioned, denoted, etc.) as abnormal/aberrant/atypical may be         used to identify energy consumption outliers associated with         voltage events. This information may be used to help quantify         the energy and demand contribution of events to the total         losses. When a voltage event occurs, equipment may         unintentionally trip off-line. The process of restarting         equipment and processes consumes energy and can (in some cases)         produce a new peak demand for the facility. Although these costs         are frequently overlooked/missed, they may be considerable over         time while contributing little to the actual production and         profitability of the operation. There may be ways to recoup some         of these costs through insurance policy coverage, tax write-offs         in some jurisdictions, and even peak demand “forgiveness” from         the utility. Perhaps most importantly, quantifying the financial         impact of voltage events to utility bills can provide incentive         to mitigate the voltage events leading to these unexpected and         potentially impactful losses.

VII. Other Evaluations and Metrics Related to Voltage Event Impact and Recovery Time

As is known, voltage events including outages are a leading global cause of business interruption-related losses. The annual estimated economic loss for medium and large businesses is estimated to be between $104 billion and $164 billion based on a study by Allianz Global. In embodiments, by incorporating additional economic metadata, it is possible to evaluate individual voltage events to determine the monetary impact of these events. Additionally, in embodiments it is possible to totalize the voltage event impacts by aggregating data and information from individual events. Some example useful financial information to help quantify the economic impact of voltage events include: average material loss/event/hour, utility rate tariffs (as discussed above), average production loss cost/event/hour, estimated equipment loss/event/hour, average 3^(rd) party costs/event/hour, or any other monetary metric related to the cost of downtime on a per event or daily/hourly/minutely basis. Using the recovery time from the calculations described above, metrics may be determined for substantially any loss that has been monetarily quantified. These metrics may be determined at a discrete IED, zone and/or system level accordingly.

A number of new voltage event-related indices are set forth herein as useful metrics for qualifying and quantifying voltage events and anomalies. While these new indices focus on voltage sags, in embodiments they may also be considered for any other voltage event or category of power quality event. Example indices include:

-   -   Mean Time Between Events (MTBE). As used herein, the term “MTBE”         is used to describe the average or expected time a system or         portion of a system is operational between events and their         subsequent recovery time. This includes both impactful and         non-impactful events, so there may or may not be a quantity of         recovery time associated with each event.     -   Mean Time Between Impactful Events (MTBIE). As used herein, the         term “MTBIE” is used to describe the average or expected time a         system or portion of a system is operational between events and         their subsequent recovery time. In embodiment, this metric is         limited to only impactful events and will likely have some         quantity of recovery time associated with each event.     -   Mean Time to Restart (MTTR). As used herein, the term “MTTR” is         used to describe the average time it takes to restart production         at a system or portion of a system (e.g., load, zone, etc.)         level. This “average time” includes all (or substantially all)         factors involved in restarting production including (but not         limited to): repairs, reconfigurations, resets,         reinitializations, reviews, retests, recalibrations, restarts,         replacing, retraining, relocating, revalidations, and any other         aspect/function/work effecting the recovery time of an         operation.     -   Sag rate. As used herein, the term “sag rate” is used to         describe the average number of voltage sag-events of a system or         portion of a system over a given time period such as hours,         months, years (or other time period).     -   Production Availability. As used herein, the term “production         availability” generally refers to the immediate readiness for         production, and is defined as the ability of a facility to         perform its required operation at a given time or period. This         metric focuses on event-driven parameter(s) and may be         determined by:

${PA}_{i} = \frac{MTBIE}{{MTBIE} + {MTTR}}$

In embodiments, systems, zones, and/or discrete IED points may be characterized by their “Number of 9's Production Up-Time,” which is an indication of the production availability exclusive of the recovery time duration. Similar to the number of 9's in the usual connotation, this metric may be determined annually (or normalized to an annual value) to provide an indication or metric of the impact of voltage events (or other events) on an operation's productivity. This metric may be useful to help identify mitigation investment opportunities and to prioritize those opportunities accordingly.

In embodiments, it is possible to use the metrics set forth above to estimate/predict recovery time based on historical recovery time information. A voltage event's magnitude, duration, location, metadata, IED characterization, or other calculated/derived data and information, for example, may be used to facilitate these estimations and predictions. This measure may be performed and provided to energy consumers at the discrete IED point, zone, and/or system level as one or more reports, texts, emails, audible indications, screens/displays, or through any other interactive means.

A few examples of supplementary metrics that may be unique to an energy consumer's operation and assist in prioritizing mitigation equipment considerations for placement, investment, etc. include:

-   -   Average Zonal Interruption Frequency Index (AZIFI). AZIFI is an         example metric that can be used to quantify zones experiencing         “the most” interruptions in an electrical system. As used         herein, AZIFI is defined as:

${AZIFI} = \frac{{number}\mspace{14mu}{of}\mspace{14mu}{zone}\mspace{14mu}{impacts}\mspace{14mu}{within}\mspace{14mu}{facility}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{system}}$

-   -   Zonal Impact Average Interruption Frequency Index (ZIAIFI).         ZIAIFI is an example metric that can be used to show trends in         zone interruptions along with number of zones affected in         electrical system. As used herein, ZIAIFI is defined as:

${ZIAIFI} = \frac{{number}\mspace{14mu}{of}\mspace{14mu}{zone}\mspace{14mu}{impacts}}{{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{had}\mspace{14mu}{at}\mspace{14mu}{least}\mspace{14mu}{one}\mspace{14mu}{impact}}$

-   -   Average Zonal Interruption Duration Index (AZIDI). AZIDI is an         example metric that can be used to indicate an overall         reliability of the system based on an average of zone impacts.         As used herein, AZIDI is defined as:

${AZIDI} = \frac{{sum}\mspace{14mu}{of}\mspace{14mu}{recovery}\mspace{14mu}{time}\mspace{14mu}{durations}\mspace{14mu}{of}\mspace{14mu}{all}\mspace{14mu}{impacted}\mspace{14mu}{zones}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{system}}$

-   -   Zonal Total Average Interruption Duration Index (ZTAIDI). ZTAIDI         is an example metric that can be used to provide an indication         of the average recovery period for zones that experienced at         least one impactful voltage event. As used herein, ZTAIDI is         defined as:

${ZTAIDI} = \frac{{sum}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{durations}\mspace{14mu}{of}\mspace{14mu}{all}\mspace{14mu}{zone}\mspace{14mu}{impacts}}{{number}\mspace{14mu}{of}\mspace{14mu}{zones}\mspace{14mu}{that}\mspace{14mu}{experienced}\mspace{14mu}{at}\mspace{14mu}{least}\mspace{14mu}{one}\mspace{14mu}{impact}}$

While the foregoing metrics focus on zone-related impacts, in embodiments some or all concepts may be reused for discrete IED points or (in some cases) system impact metrics. It is understood that the purpose here is to document examples of the ability to create useful metrics for energy consumers and their operations; not to define every possible metric or combination thereof.

It is also understood that each of the metrics discussed above may be further determined and partitioned for upstream, downstream, internal (e.g., facility), and external (e.g., utility) voltage event sources as appropriate. The latter two mentioned (internal/external) may require some level of hierarchical classification of the IED and/or electrical system. Other classifications of hierarchies (e.g., protection layout schemes, separately derived sources, processes or sub-systems, load types, sub-billing groups or tenants, network communications schemes, etc.) may be used to create/derive additional useful metrics as needed to better evaluate the impact of voltage events on a facility's operation, for example. Outputs from embodiments of this disclosure may be provided by one or more reports, texts, emails, audible indications, screens/displays, or through any other interactive means. Indications may be provided at the IED, on-site software, cloud, gateway, or other monitoring system component and/or accessory. In embodiments, the outputs and indications may be generated by circuitry and systems according to the disclosure in response to the circuitry and systems receiving and processing respective inputs.

VIII. Voltage Event Recovery Status Tracking

An example method according to the disclosure for reducing recovery time periods includes providing a method of tracking the recovery as it progresses. By identifying and monitoring the recovery periods through discrete IEDs, zones, hierarchies, and/or the system in real-time, the energy consumer (and the systems and methods disclosure herein) is/are better able to identify, manage, and expedite the recovery process for an event throughout their facility. Event recovery tracking allows energy consumers to understand the status of the recovery and make better and quicker decisions to facilitate its recovery. This embodiment would also allow the energy consumer to review historical data to make recovery improvements, produce and/or update recovery procedures, identify zonal recovery constrictions, troublesome equipment, and so forth to improve future event recovery situations (and thus, increase system uptime and availability). Alarming capabilities may be incorporated into recovery situations to provide indications of constraining locations within zones or the facility. Historical recovery metrics or some other configured setpoints may be used to determine recovery alarm threshold settings for IEDs, system software, and/or cloud, and outputs from embodiments of this disclosure may be provided by one or more reports, texts, emails, audible indications, screens/displays, or through any other interactive means.

IX. Developing Various Baselines Related to Voltage Events

Another example method for determining expected recovery times uses factors such as market segments and/or customer types, processes-based evaluations, and/or load and equipment types to determine the expected recovery times. By defining recovery times based on these and other factors, for example, a recovery time baseline or reference can be developed with respect to a voltage event's magnitude, duration, percent load impacted, and/or any other electrical parameter, metadata, or IED specification. The baselines/references may be used to set recovery alarm thresholds, assess recovery time performance and identify opportunities for improvement, estimate actual vs. expected recovery time and costs, improve accuracy of estimates for impactful voltage events, and so forth. Actual historical voltage event impact and recovery time data may be used to produce relevant models through various means including statistical analyses (and/or analytics) and evaluations, simple interpolation/extrapolation, and/or any other method that produces a reasonable typical value(s). Baseline/reference models may range from simple to complex, and may be created or determined for discrete IED locations, zones, or entire systems, and outputs from embodiments of this disclosure may be provided by one or more reports, texts, emails, audible indications, screens/displays, or through any other interactive means.

X. Evaluating Voltage Event for Similarities to Identify Repetitive Behavior

In embodiments, evaluating voltage events across an electrical system to examine event similarity may be useful for energy consumers. Similarities may be in event time of occurrence, seasonality, recovery time characteristics, behavior of electrical parameters, behavior of zonal characteristics, behavior of operational processes, and/or any other notable behaviors or commonalities. Identifying repetitive behaviors and/or commonalities may be an important tactic for prioritizing and resolving voltage event effects. Moreover, analysis/analytics of historical data may provide the ability to predict the system impact and recovery time due to a voltage event after the initial onset of said voltage event.

XI. Voltage Event Forecasting

As mentioned in previous embodiments of the disclosure, it is important to be able to identify beneficial opportunities for energy consumers to mitigate voltage events. Another metric that may be considered is forecasting an estimated number of interruptions, estimated impact, and total recovery time (and associated costs). In embodiments, this metric may be extremely useful for planning purposes, support of capital investment opportunities in voltage event mitigation equipment, and even to forecast expected savings for installing said mitigation equipment. These forecasts may be evaluated at a later point in time to ascertain their accuracy and to fine-tune forecasts and expectations going forward.

XII. Other Graphs and Diagrams Related to Voltage Event Impact and Recovery Time

Aside from the various plots (or graphs) discussed in connection with the embodiments described above, there are other additional useful methods to display data related to voltage events. The graphs described below in connection with FIGS. 41-44, for example, are only a few examples of displaying data in a useful format; there may be many other methods to present voltage event data in a meaningful way that can benefit energy consumers. Graphs, charts, tables, diagrams, and/or other illustrative techniques, for example, may be used to summarize, compare, contrast, validate, order, trend, demonstrate relationships, explain, and so forth. These data types may be real-time, historical, modeled, projected, baseline, measured, calculated, statistical, derived, summarized, and/or estimated. Graphs may also be any dimension (e.g., 2-D, 3-D, etc.), color, shade, shape (e.g., line, bar, etc.), etc. to provide a unique and useful perspective.

FIG. 41 illustrates an example of the load impact versus recovery time for a single event. The green area is indicative of normal or expected range of operational parameters, the shaded orange area is highlighting the recovery time period, and the black line is the load as a function of time. FIG. 42 illustrates an example of a series of impactful events versus their recovery time from a single IED (multiple IEDs could also be used here). In this example, the green area is indicative of normal or expected operational parameters, and the shaded orange highlights the periods when the system has experienced an impactful event and experienced a recovery period. FIG. 43 illustrates an example of additional data being integrated with the data shown in FIG. 41. In this example, the green area is indicative of normal or expected range of operational parameters, the shaded orange is highlighting the recovery time period, the black line is showing the load as a function of time, the dashed pink line is showing the expected load as a function of time, and the dashed blue line shows a typical pre-event profile. As a rule of thumb, the behavior of upstream events may be more unpredictable than downstream events over time. FIG. 44 illustrates an example of pre/during/post-event percent of load impact versus recovery time for a voltage event. Again, different variables, metrics parameters, characteristics, etc. may be graphed, illustrated, etc. shown as needed or useful.

XIII. Aggregation/Consolidation of Voltage Event Impact and Recovery Time Data

As is known, voltage events are often extensive, impacting multiple loads, processes, and even the entire system concurrently. In embodiments, metering systems according to the disclosure may exhibit multiple alarms from different IEDs located across the facility. Source events generally impact the entire system, for example, resulting in every (or substantially every) capable IEDs indicating an event has occurred.

In embodiments, aggregating/consolidating the multitude of voltage event data, alarms and impacts across a system is important for several reasons. First, many energy consumers have a tendency to ignore “alarm avalanches” from monitoring systems, so aggregating/consolidating voltage event data decreases the number alarms the energy consumer has to review and acknowledge. Second, the data from a flurry of alarms is often the result of one voltage event coming from the same root cause. In this case, it is much more efficient to reconcile all coincident voltage events captured by multiple IEDs into a single event for reconciliation. Third, bundled voltage events are much easier to analyze than independent voltage events as most of the relevant data and information is available in one place. For the sake of brevity, there are many other reasons to aggregate/consolidate voltage events not listed here.

The ability to aggregate/consolidate the impact of voltage events and their often-accompanying recovery times is important because it helps avoid redundancy of event data. Redundant event data can skew metrics and exaggerate conclusions, which may results in flawed decisions. This disclosure focuses on three layers of aggregation/consolidation within electrical systems: IED, zonal and system.

In embodiments, the first layer (IED) requires minimal aggregation/consolidation because data is acquired from a single point/device and (hopefully) the device shouldn't be producing redundant information within itself from voltage events. In some cases, there may be somewhat superfluous alarm information from a single device. For example, a three-phase voltage event may provide one alarm for each of the three phases experiencing the voltage event. Moreover, an alarm may be triggered for both the event pickup and dropout, resulting in six total voltage event alarms (a pickup and dropout alarm for each of the three phases). While this example of alarm abundance may be bothersome and confusing, many devices and monitoring systems already aggregate/consolidate multiple event alarms as just described into a single event alarm. In some embodiments, a single voltage event alarm may be provided from each IED for each voltage event that occurs in the electrical system.

It was mentioned above that a voltage event often impacts multiple IEDs within a monitoring system; specifically, those that are capable of capturing anomalous voltage conditions. Since zones and systems typically consist of multiple IEDs, the need to aggregate/consolidate the impact and subsequent repercussions of voltage events lies with these two (zones and systems). Although a zone may encompass an entire system, zones are configured as a subset/sub-system of the electrical and/or metering system. However, because zones and systems both generally consist of multiple devices, they will be treated similarly.

In embodiments, there are different methods/techniques to aggregate/consolidate zones. A first example method includes evaluating the voltage event impact and recovery time from all IEDs within a particular zone and attributing the most severe impact and recovery time from any single IED within that zone to the entire zone. Because the event impact and recovery time are independent variables, and therefore may be derived from different IEDs, these two variables should be treated independently from each other. Of course, it would be important to track which zonal device was considered/recognized as having experienced the most severe impact and which zonal device experienced the longest recovery time. This same approach could be used for systems by leveraging the conclusions generated from the zone evaluations. Ultimately, the recovery time for a system is not completed until all relevant IEDs indicate that is the case.

A second example method includes assessing a voltage event within a zone by using statistical evaluations (e.g., average, impact and average recovery time, etc.) from all IEDs with a particular zone. In this case, the severity of a voltage event may be determined for the entire zone by statistically appraising data from all IEDs and providing results to represent the entire zone for each particular voltage event. Statistical determinations including means, standard deviations, correlations, confidence, error, accuracy, precision, bias, coefficients of variation, and any other statistical methods and/or techniques may be employed to aggregate/consolidate the data from multiple IEDs to a representative value or values for the zone. The same statistical approach may be used to combine zones into representative metrics/values for system impact and recovery time. Again, the recovery time for a system will be contingent on each relevant IED indicating that is the case.

Another example method to evaluate voltage events is by load-type. In embodiments, the energy consumer (or systems and method disclosed herein) may choose to categorize and aggregate/consolidate loads by similarity (e.g., motors, lighting, etc.) regardless of their location within the facility's electrical system, and evaluate the impact and recovery time of those loads accordingly. It would also be possible to evaluate voltage events by their respective processes. By aggregating/consolidating loads (regardless of type, location, etc.) associated with the same process, the impact and recovery time could be quantified for said process. Another method to aggregate/consolidate voltage events is by sources and/or separately derived sources. This approach would help quantify the impact and recovery time of a voltage event as it related to the energy source within the facility (or out on the utility network). Other useful and logical methods to aggregate/consolidate voltage event information from two or more IEDs may be considered as well (e.g., by building, by product, by cost, by maintenance, and so forth).

In embodiments, a fundamental purpose of aggregating/consolidating voltage event data is to identify opportunities to decrease these events' overall impact on the energy consumer's business to reduce downtime and make it more profitable. One or more of the methods (or combinations of methods) described herein may be used to meet this objective. It may be useful or even required to have one or more of these methods configured by the energy consumer (or surrogate), or the system and methods disclosed herein. The ability to consider the voltage event impact and recovery time at discrete IEDs is not mutually exclusive from any approach to consider and evaluate aggregated/consolidated voltage event impact and recovery time.

Another interesting prospect would be evaluating the performance of the energy consumer's operation after the initial voltage event occurs. For example, a voltage event may result in one load tripping off-line. Shortly after, another related load may also trip off-line as a result of the first load tripping; not due to another voltage event. The extent of this chain reaction/propagation would be of interest when determining consequences of providing ride-through mitigation for the first load. In this example, providing a timeline of load reactions over the recovery period due to the original voltage event may be prudent to help minimize the overall impact of voltage events on the energy consumer's operation.

In embodiments, outcomes from analyses of the voltage and current data apply to the point in the network where the IED capturing the data is connected. Each IED in the network may typically yield distinct analyses of the event, assuming each IED is uniquely placed. As used herein, the term “uniquely placed” generally refers to the location of the installation within the electrical system, which impacts impedance, metered/connected loads, voltage levels, and so forth. In some cases, it may be possible to interpolate or extrapolate voltage event data on a case-by-case basis.

In embodiments, in order to accurately characterize power quality events (e.g., voltage sags) and their subsequent network impact(s), it is important to measure the voltage and current signals associated with the event. Voltage signals can be used to characterize the event, current signals can be used to quantify the event's impact, and both voltage and current can be used to derive other relevant electrical parameters related to this disclosure. Although outcomes from analyses of the voltage and current data apply to the point in the network where the IED capturing the data is connected, it may be possible to interpolate and/or extrapolate voltage event data on a case-by-case basis. Each IED in the network typically yields distinct analyses of the event, assuming each IED is uniquely placed.

In embodiments, there are multiple factors that can influence the impact (or non-impact) of a voltage sag. The impedance of the energy consumer's electrical system may cause voltage events to produce more severe voltage sags deeper into the system hierarchy (assuming a radial-fed topology). Voltage event magnitudes, durations, fault types, operational parameters, event timing, phase angles, load types, and a variety of other factors related to functional, electrical, and even maintenance parameters can influence the effects of voltage sag events.

It is understood that any relevant information and/or data derived from IEDs, customer types, market segment types, load types, IED capabilities, and any other metadata may be stored, analyzed, displayed, and/or processed in the cloud, on-site (software and/or gateways), or in a IED in some embodiments.

Referring to FIGS. 45-48, several flowcharts (or flow diagrams) are shown to illustrate various methods of the disclosure. Rectangular elements (typified by element 4505 in FIG. 45), as may be referred to herein as “processing blocks,” may represent computer software and/or IED algorithm instructions or groups of instructions. Diamond shaped elements (typified by element 4525 in FIG. 45), as may be referred to herein as “decision blocks,” represent computer software and/or IED algorithm instructions, or groups of instructions, which affect the execution of the computer software and/or IED algorithm instructions represented by the processing blocks. The processing blocks and decision blocks can represent steps performed by functionally equivalent circuits such as a digital signal processor circuit or an application specific integrated circuit (ASIC).

The flowcharts do not depict the syntax of any particular programming language. Rather, the flowcharts illustrate the functional information one of ordinary skill in the art requires to fabricate circuits or to generate computer software to perform the processing required of the particular apparatus. It should be noted that many routine program elements, such as initialization of loops and variables and the use of temporary variables are not shown. It will be appreciated by those of ordinary skill in the art that unless otherwise indicated herein, the particular sequence of blocks described is illustrative only and can be varied. Thus, unless otherwise stated, the blocks described below are unordered; meaning that, when possible, the blocks can be performed in any convenient or desirable order including that sequential blocks can be performed simultaneously and vice versa. It will also be understood that various features from the flowcharts described below may be combined in some embodiments. Thus, unless otherwise stated, features from one of the flowcharts described below may be combined with features of other ones of the flowcharts described below, for example, to capture the various advantages and aspects of systems and methods associated with dynamic tolerance curves sought to be protected by this disclosure.

Referring to FIG. 45, a flowchart illustrates an example method 4500 for managing power quality events (or disturbances) in an electrical system that can be implemented, for example, on a processor of an IED (e.g., 121, shown in FIG. 1A) and/or a processor of a control system associated with the electrical system. Method 4500 may also be implemented remote from the IED and/or control system in a gateway, cloud, on-site software, etc.

As illustrated in FIG. 45, the method 4500 begins at block 4505, where voltage and/or current signals (or waveforms) associated with one or more loads (e.g., 111, shown in FIG. 1A) in an electrical system are measured and data is captured, collected, stored, etc. by an IED (and/or control system) coupled to the loads.

At block 4510, electrical measurement data from the voltage and/or current signals is processed to identify at least one power quality event associated with one or more of the loads. In some embodiments, identifying the at least one power quality event may include identifying: (a) a power quality event type of the at least one power quality event, (b) a magnitude of the at least one power quality event, (c) a duration of the at least one power quality event, and/or (d) a location of the at least one power quality event in the electrical system, for example. In embodiments, the power quality event type may include one of a voltage sag, a voltage swell, and a voltage transient.

At block 4515, an impact of the at least one identified power quality event on one or more of the loads is determined. In some embodiments, determining the impact of the at least one identified power quality event includes measuring one or more first parameters (e.g., “pre-event”parameters) associated with the loads at a first time (e.g., a time prior to the event), measuring one or more second parameters (e.g., “post-event” parameters) associated with the loads at a second time (e.g., a time after the event), and comparing the first parameters to the second parameters to determine the impact of the at least one identified power quality event on the loads. In embodiments, the power quality event(s) may be characterized as an impactful event or a non-impactful event based, at least in part, on the determined impact of the event(s). An impactful event may, for example, correspond to an event that interrupts operation (or effectiveness) of the loads and/or the electrical system including the loads. This, in turn, may impact an output of the system, for example, the production, quality, rate, etc. of a product generated by the system. In some embodiments, the product may be a physical/tangible object (e.g., a widget). Additionally, in some embodiments the product may be a non-physical object (e.g., data or information). A non-impactful event, by contrast, may correspond to an event that does not interrupt (or minimally interrupts) operation (or effectiveness) of the loads and/or the electrical system including the loads.

At block 4520, the at least one identified power quality event and the determined impact of the at least one identified power quality event are used to generate or update an existing tolerance curve associated with the one or more of the loads. In embodiments, the tolerance curve characterizes a tolerance level of the loads to certain power quality events. For example, the tolerance curve (e.g., as shown in FIG. 4) may be generated to indicate a “prohibited region”, a “no damage region” and a “no interruption in function region” associated with the loads (and/or electrical system), with the respective regions corresponds to various tolerance levels of the loads to certain power quality events. The tolerance curve may be displayed on a graphical user interface (GUI) (e.g., 230, shown in FIG. 1B) of the IED and/or or GUI of the control system, for example. In embodiments where a tolerance curve has already been generated prior to block 4520, for example, due to there being an existing tolerance curve, the existing tolerance curve may be updated to include information derived from the at least one identified power quality event and the determined impact of the at least one identified power quality event. An existing tolerance curve may exist, for example, in embodiments in which a baseline tolerance curve exists or in embodiments in which a tolerance curve has already been generated using method 4500 (e.g., an initial tolerance curve generated in response to a first or initial power quality event). In other words, in embodiments a new tolerance curve is typically not generated after each identified power quality event, but rather each identified power quality event may result in updates being made to an existing tolerance curve.

At block 4525, which is optional in some embodiments, it is determined if the impact of the at least one identified power quality event exceeds a threshold or falls outside of a range or region (e.g., “no interruption in function region”) indicated in the tolerance curve. If it is determined that the impact of the at least one identified power quality event falls outside of the range indicated in the tolerance curve (e.g., the event results in an interruption to the function of a load as measured by an electrical parameter or indicated by some external input), the method may proceed to block 4530. Alternatively, if is determined that the impact of the at least one identified power quality event does not fall outside of a range indicated in the tolerance curve (e.g., the event does not result in an interruption in a function of a load), the method may end in some embodiments. In other embodiments, the method may return to block 4505 and repeat again. For example, in embodiments in which it is desirable to continuously (or semi-continuously) capture voltage and/or current signals and to dynamically update the tolerance curve in response to power quality events identified in these captured voltage and/or current signals, the method may return to block 4505. Alternatively, in embodiments in which it is desirable to characterize power quality events identified in a single set of captured voltage and/or current signals, the method may end.

Further, in embodiments the event information may be used to adjust (e.g., expand) the “no interruption in function” region, for example, to generate a custom tolerance curve for the specific IED location (similar to FIG. 2). It is to be appreciated that characterizing the electrical system at certain points is extremely useful to users because they can better understand the behavior of their system.

In some embodiments, the range indicated in the tolerance curve is a predetermined range, for example, a user configured range. In other embodiments, the range is not predetermined. For example, I may choose to have no “no interruption in function” region and say anything deviating from a nominal voltage needs to be evaluated. In this case, the voltage may range all over the place and I may have dozens of power quality events; however, my load may not experience any interruptions. Thus, these events are not considered impactful. In this case, I widen/expand my “no interruption” region from basically the nominal voltage outwards to the point where these events do start to perturbate my loads (based on measured load impact pre-event vs. post event).

In other words, the invention is not limited to the ITIC curve (or any other predetermined range or curve(s)). Rather, embodiments of the invention call for “creating” a custom voltage tolerance curve for a specific location (i.e., where the IED is located) within the electrical system or network. The curve may be based on the ITIC curve, the SEMI curve, or any number of other curves. Additionally, the curve may be a custom curve (i.e., may not be based on a known curve, but rather may be developed without an initial reference or baseline). It is understood that a predetermined tolerance curve is not required for this invention, rather it just used to explain the invention (in connection with this figure, and in connection with figures described above and below).

At block 4530, which is optional is some embodiments, an action affecting at least one component of the electrical system may be automatically performed in response to the determined impact of the at least one identified power quality event being outside of the range indicted in the tolerance curve. For example, in some embodiments a control signal may be generated in response to the determined impact of the at least one identified power quality event being outside of the range, and the control signal may be used to affect the at least one component of the electrical system. In some embodiments, the at least one component of the electrical system corresponds to at least one of the loads monitored by the IED. The control signal may be generated by the IED, a control system, or another device or system associated with the electrical system. As discussed in figures above, in some embodiments the IED may include or correspond to the control system. Additionally, in some embodiments the control system may include the IED.

As another example, an action that may be affected at block 4530 is starting and stopping a timer to quantify a length (or duration) of the impact to production, for example, in a facility with which the impact is associated. This will help a user make better decisions regarding operation of the facility during atypical conditions.

Subsequent to block 4530, the method may end in some embodiments. In other embodiments, the method may return to block 4505 and repeat again (for substantially the same reasons discussed above in connection with block 4525). In some embodiments in which the method ends after block 4530, the method may be initiated again in response to user input and/or a control signal, for example.

Referring to FIG. 46, a flowchart illustrates an example method 4600 for quantifying power quality events (or disturbances) in an electrical system that can be implemented, for example, on a processor of an IED (e.g., 121, shown in FIG. 1A) and/or a processor of a control system. Method 4600 may also be implemented remote from the IED in a gateway, cloud, on-site software, etc. This method 4600 evaluates voltage and/or current signals measured and captured by the IED to determine whether the electrical system was impacted (e.g., at the IED(s) level) using pre-event/post-event power characteristics. In embodiments, it is possible to determine a recovery time using a threshold (e.g., the post-event power is 90% of the pre-event power). This allows us to quantify the impact of a power quality disturbance to a load(s), process(es), system(s), facility(ies), etc.

As illustrated in FIG. 46, the method 4600 begins at block 4605, where voltage and/or current signals (or waveforms) are measured and captured by an IED.

At block 4610, the voltage and/or current signals are processed to identify a power quality event associated with one or more loads (e.g., 111, shown in FIG. 1A) monitored by the IED. In some embodiments, pre-event, event and post-event logged data may also be used to identify the power quality event. The pre-event, event and post-event logged data may, for example, be stored on a memory device associated with the IED and/or gateway, cloud and/or on-site software application.

At block 4615, pre-event parameters are determined from the voltage and/or current signals. In embodiments, the pre-event parameters correspond to substantially any parameters that can be directly measured and/or derived from voltage and current including, but not limited to, power, energy, harmonics, power factor, frequency, event parameters (e.g., time of disturbance, magnitude of disturbance, etc.), etc. In embodiments, pre-event data can also be derived from “statistical norms.” Metadata may also be used to help derive additional parameters accordingly.

At block 4620, an impact of the power quality event is determined, measured or calculated. In embodiments, the event impact is calculated based on pre-event vs. post-event parameters. In embodiments, this includes both the characteristics of the event (i.e., magnitude, duration, disturbance type, etc.) and its impact to load(s), process(es), system(s), facility(ies), etc. at the metered point in the system.

At block 4625, recovery thresholds (or conditions) are compared to real-time parameters. In embodiments, the recovery thresholds may correspond to a percent of pre-event conditions to be considered as a system, sub-system, process, and/or load recovery condition. In embodiments, industry standards, market segment recommendations, historical analysis, independently determined variables, and/or load characteristics may be used to provide the recovery thresholds. Additionally, statistical norms may be used to provide the recovery thresholds. In embodiments, the recovery thresholds are configured (e.g., pre-configured) recovery thresholds that are stored on a memory device associated with the IED. An alternative approach is to pass all voltage event information to the cloud or on-site software and then filter it there using recovery thresholds. In this case, the recovery thresholds would be stored in the cloud or on-site and not in the IED.

At block 4630, the IED determines if the real-time parameters meet the recovery thresholds (or conditions). If the IED determines that the real-time parameters meet the recovery thresholds, the method proceeds to block 4635. Alternatively, if the IED determines that the real-time parameters do not meet the recovery thresholds, the method may return to block 4625, and block 4625 may be repeated again. In embodiments, the output here is to determine the recovery time; therefore, it may stay in the loop until the post-event levels meet a predetermined threshold.

At block 4635, the IED calculates a recovery time from the power quality event. In embodiments, the recovery time is calculated from a time associated with the power quality event (e.g., an initial occurrence of the power quality event) until a time the recovery thresholds are met.

At block 4640, an indication of the power quality disturbance (or event) is provided at an output of the IED. In embodiments, the indication may include one or more reports and/or one or more control signals. The report may be generated to include information from any discrete IED of the electrical system including: recovery time, impact on power, costs associated with the event impact, I/O status changes, time of event/time of recovery, changes in voltages/currents, imbalance changes, areas impacted, etc. In embodiments, recovery time and impact may be based on data from one or more IEDs. The reports may be provided to customer, sales teams, offer management, engineering teams, and/or any other interested party, etc. The control signals may be generated to control one or more parameters or characteristics associated with the electrical system. As one example, the control signals may be used to adjust one or more parameters associated with load(s) which the IED is configured to monitor.

At block 4640, the indication of the power quality disturbance (and other data associated with method 4600) may also be stored. In some embodiments, the indication may be stored locally, for example, on a same site as the IED (or on the IED device itself). Additionally, in some embodiments the indication may be stored remotely, for example, in the cloud and/or on-site software. After block 4640, the method 4600 may end.

Referring to FIG. 47, a flowchart illustrates an example method 4700 for expanded qualified lead generation for power quality. Similar to method 4600 described above in connection with FIG. 46, for example, in embodiments method 4700 can be implemented on a processor of an IED and/or a processor of a control system. Method 4700 may also be implemented remote from the IED in a gateway, cloud, on-site software, etc. In embodiments, by evaluating pre-event/post-event power characteristics of power quality events, it is possible to quantify the susceptibility of the electrical system at metered points to power quality disturbances. This information could be used to identify product offerings for mitigative solutions and provide better qualified leads to organizations marketing those solutions. In embodiments, method 4700 may also be used for energy savings opportunities (e.g., power factor correction, increased equipment efficiency, etc.) when a power quality event occurs.

As illustrated in FIG. 47, the method 4700 begins at block 4705, where voltage and/or current signals (or waveforms) are measured and captured by an IED.

At block 4710, the voltage and/or current signals are processed to identify a power quality event associated with one or more loads monitored by the IED. In some embodiments, pre-event, event and post-event logged data may also be used to identify the power quality event. The pre-event, event and post-event logged data may, for example, be stored on a memory device associated with the IED and/or gateway, cloud and/or on-site software application.

At block 4715, pre-event parameters are determined from the voltage and/or current signals. In embodiments, the pre-event parameters correspond to substantially any parameters that can be directly measured and/or derived from voltage and current including, but not limited to, power, energy, harmonics, power factor, frequency, event parameters (e.g., time of disturbance, magnitude of disturbance, etc.), etc. In embodiments, pre-event data can also be derived from “statistical norms.” Metadata may also be used to help derive additional parameters accordingly.

At block 4720, an impact of the power quality event is calculated. In embodiments, the event impact is calculated based on pre-event vs. post-event parameters. In embodiments, this includes both the characteristics of the event (i.e., magnitude, duration, disturbance type, etc.) and its impact to load(s), process(es), system(s), facility(ies), etc. at the metered point in the system.

At block 4725, event characteristics are compared to mitigative solutions (e.g., product solutions). In embodiments, there may be a library of design and applications criteria for solutions to mitigate issues associated with a power quality event or disturbance. The library of design and applications criteria for solutions may be stored on a memory device associated with the IED, or accessed by the IED (e.g., remotely, via the cloud). In some embodiments, block 4725 may be performed in the cloud or on-site software. That way the energy consumer is able to see everything from a system level.

At block 4730, the IED determines if a particular entity (e.g., Schneider Electric) provides a mitigative solution for specific event. If the IED determines that the particular entity provides a mitigative solution for the specific event, the method proceeds to a block 4635. Alternatively, if the IED determines that the particular entity does not provide a mitigative solution for the specific event, the method proceeds to a block 4750. In some embodiments, the “IED” may be defined as being in the cloud or on-site (yet remote from the meter). In embodiments, it may be prudent to put the solutions and much of the analysis in the cloud or on-site software because it's easier to update, the energy consumer has easier access to it, and it provides an aggregate system view.

At block 4735, a list of solutions provided by the particular entity is built for the specific event or issue (or type of event or issue). At block 4740, a report is generated and provided to customers, sales teams associated with the particular entity or other appropriate representatives of the entity. In embodiments, the report may include information from any discrete metering device (or as a system) including: recovery time, impact on power, I/O status changes, time of event/time of recovery, changes in voltages/currents, changes in phase balance, processes and/or areas impacted, etc. Report may include information on SE solution (e.g., customer facing literature, features and benefits, technical specifications, cost, etc.), approximate solution size required for given event (or event type), comparisons to external standards, placement, etc. Electrical and/or metering system hierarchy and/or other metadata (e.g., load characteristics, etc.) may be used to assist evaluation.

At block 4745, the report (and other information associated with the method 4700) may be stored. In some embodiments, the report may be stored locally, for example, on a same site as the IED (or on the IED device itself). Additionally, in some embodiments the report may be stored remotely, for example, in the cloud. In embodiment, blocks 4740 and 4745 may be performed substantially simultaneously.

Returning now to block 4730, if it is determined that the particular entity does not provide a mitigative solution for the specified event, the method proceeds to a block 4750. At block 4750, event parameters and/or characteristics (and other information associated with the method 4700) may be stored (e.g., locally and/or in the cloud). At block 4755, a report is generated based, at least in part, on select information stored at block 4750. In embodiments, the report may include an evaluation of energy consumer impacts and needs for potential future solution development, third-party solutions, etc. After block 4755 (or blocks 4740/4745), the method 4700 may end.

Referring to FIG. 48, a flowchart illustrates an example method 4800 for dynamic tolerance curve generation for power quality. Similar to methods 4500, 4600 and 4700 described above, in embodiments method 4800 can be implemented on a processor of an IED and/or a processor of a control system. Method 4800 may also be implemented remote from the IED in a gateway, cloud, on-site software, etc. In embodiments, by evaluating pre-event/event/post-event power characteristics of power quality events, it is possible (over time) to automatically develop a custom event tolerance curve for substantially any given energy consumer. This is extremely useful to help energy consumers identify, characterize, analyze and/or desensitize their system to power quality events.

As illustrated in FIG. 48, the method 4800 begins at block 4805, where voltage and/or current signals (or waveforms) are measured and captured by an IED.

At block 4810, the voltage and/or current signals are processed to identify a power quality event associated with one or more loads monitored by the IED. In some embodiments, pre-event, event and post-event logged data may also be used to identify the power quality event. The pre-event, event and post-event logged data may, for example, be stored on a memory device associated with the IED and/or gateway, cloud and/or on-site software application.

At block 4815, pre-event parameters are determined from the voltage and/or current signals. In embodiments, the pre-event parameters correspond to substantially any parameters that can be directly measured and/or derived from voltage and current including, but not limited to, power, energy, harmonics, power factor, frequency, event parameters (e.g., time of disturbance, magnitude of disturbance, etc.), etc. In embodiments, pre-event data can also be derived from “statistical norms.” Metadata may also be used to help derive additional parameters accordingly.

At block 4820, an impact of the power quality event is determined. In embodiments, the event impact is calculated based on pre-event vs. post-event parameters. In embodiments, this includes both the characteristics of the event (i.e., magnitude, duration, disturbance type, etc.) and its impact to load(s), process(es), system(s), facility(ies), etc. at the metered point in the system.

At block 4825, disturbance thresholds (or conditions) are compared to the determined impact of the event. In embodiments, the disturbance thresholds may correspond to a percent change between pre-event and post-event conditions to be considered a “significant” system, sub-system, process, and/or load disturbance. For example, a 5% reduction in load due to an electrical (or other) event may be considered “significant.” In embodiments, the disturbance thresholds are configured (e.g., pre-configured) disturbance thresholds that are stored on a memory device associated with the IED and/or gateway, cloud and/or on-site software application.

At block 4830, the IED determines if the system, sub-system, process, facility and/or load experienced (or is experiencing) a “significant” disturbance (e.g., based on the comparison at block 4825). If the IED determines that the system, sub-system, process, facility and/or load(s) experienced a “significant” disturbance, the method proceeds to a block 4835. Alternatively, if the IED determines that the system, sub-system, process, facility and/or load(s) has not experienced a “significant” disturbance, the method proceeds to a block 4840.

At block 4835, a disturbance point is generated and plotted as perturbative (e.g., impacting the system, sub-system, process, facility and/or load(s), for example). At block 4845, a baseline tolerance curve (e.g., SEMI-F47, ITIC, CBEMA, etc.) is modified, changed and/or customized) based on characteristics associated with the specific recorded disturbance (here, at block 4835).

Alternatively, at block 4840, in response to the IED determining that the system, sub-system, process, facility and/or load has not experienced a “significant” disturbance, a disturbance point is generated and plotted as non-perturbative (e.g., not impacting the system, sub-system, process, facility and/or load(s), for example). At block 4845, the baseline tolerance curve is modified, changed and/or customized based on the characteristics associated with specific recorded disturbance (here, at block 4840). For example, lines in the curve may be moved between “no interruption region” and “no damage/prohibited region.” Alternatively, the lines in the curve may not be moved at all.

At block 4850, a report is generated. In embodiments, the report may include information from substantially any discrete IED (or as a system) including: recovery time, impact on power, I/O status changes, time of event/time of recovery, changes in voltages/currents, imbalance changes, areas and loads impacted, etc. The report may include updated graphs of tolerance curve(s), highlighted changes in curve(s), recommended mitigation solution(s), etc.

At block 4855, which is optional in some embodiments, at least one alarm setting may be updated at discrete metering point(s) to match the new tolerance curve (e.g., generated at block 4845). At block 4860, the new tolerance curve (and other information associated with the method 4800) may be stored (e.g., locally, in a gateway, on-site software, and/or in the cloud). In some embodiments, two or more of blocks 4850, 4855 and 4860 may be performed substantially simultaneously. After blocks 4850, 4855, and 4860, the method 4800 may end.

In general, equipment (e.g., a load or other electrical infrastructure) is designed to have a rated voltage and recommended operational range, as illustrated in FIGS. 49 and 50. The rated voltage is the desired voltage magnitude/level for optimal equipment operation. Additionally, the recommended operational range is the area surrounding the rated voltage (above and below the rated voltage) where the equipment may still successfully operate continuously, although not necessarily optimally (e.g., lower efficiency, additional heating, higher currents, etc.). IED voltage event alarm thresholds (also referred to herein as “alarm thresholds” for simplicity) are typically configured (but not always) to align with the recommended operational range so that excursions beyond the recommended operational range may be measured, captured and stored. This is because a strong correlation exists with excessive voltage excursions and temporary or permanent damage to the equipment experiencing these excursions. Additionally, voltage excursions may lead to operational issues, interruptions, loss of data, and/or any other number of impacts to equipment, processes, and/or operations.

While the “recommended operational range” of loads, processes, and/or systems is typically associated with a voltage magnitude, the duration of these excursions is also an important consideration. For example, a 1-millisecond voltage excursion of +10% outside of the recommended operational range may not adversely impact the operation of a load, process, and/or system, nor impact its expected operational life. Alternatively, a 20-millisecond voltage excursion of +10% outside of the recommended operational range may cause the same load, process, and/or system to experience an interruption and/or reduce its life expectancy (by some extent).

FIGS. 49 and 50 illustrate two representations of the same concept. Namely, FIG. 49 shows a rms waveform and FIG. 50 shows an instantaneous waveform. The rms waveform shown in FIG. 49 is derived from the instantaneous waveform data shown in FIG. 50 using a well-known equation (root-mean-square) calculation. Both waveform representations are useful for analyzing power and energy-related issues and troubleshooting power quality problems. Each respective graphic illustrates an exemplary voltage rating, upper alarm threshold, and lower alarm threshold for a theoretical load, process and/or system. In this case, the recommended operational range (shaded area) is assumed to align with the bounds of the upper and lower alarm thresholds, respectively.

Referring to FIG. 51, a flowchart illustrates an example method 5100 for characterizing power quality events in an electrical system that can be implemented, for example, on a processor of at least one IED (e.g., 121, shown in FIG. 1A). Method 5100 may also be implemented remote from the at least one IED in a gateway, cloud-based system, on-site software, or another head-end system in some embodiments.

As illustrated in FIG. 51, the method 5100 begins at block 5105, where energy-related signals are measured and data is captured, collected, stored, etc. by at least one first IED of a plurality of IEDs in an electrical system. The at least one first IED is installed at a first metering point (e.g., a physical metering point) in the electrical system (e.g., metering point M₁, shown in FIG. 30F).

At block 5110, energy-related signals are measured and data is captured, collected, stored, etc. by at least one second IED of the plurality of IEDs in the electrical system. The at least one second IED is installed at a second metering point (e.g., a physical metering point) in the electrical system (e.g., metering point M₂, shown in FIG. 30F).

In some embodiments, the energy-related signals captured by the at least one first IED and the energy-related signals captured by the at least one second IED include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages.

At block 5115, electrical measurement data for at least one first virtual meter in the electrical system is derived from (a) electrical measurement data from or derived from the energy-related signals captured by the at least one first IED, and (b) electrical measurement data from or derived from the energy-related signals captured by the at least one second IED. In embodiments, the at least one first virtual meter is derived or located at a third metering point in the electrical system (e.g., metering point V₁, shown in FIG. 30F). In embodiments, the third metering point (e.g., a virtual metering point) is different from both the first metering point and the second metering point.

In some embodiments, the electrical measurement data for the at least one first virtual meter may be derived based on a known location of the at least one first virtual meter with respect to the at least one first IED and the at least one second IED. For example, as described above in connection with FIGS. 30B-30I, the electrical measurement data for a virtual meter (e.g., the at least one first virtual meter) may be derived based on known locations of, and parent-child relationship(s) between, the virtual meter and other meters (e.g., IEDs) in the electrical system.

At block 5120, the derived electrical measurement data for the at least one first virtual meter is used to generate or update a dynamic tolerance curve associated with the third metering point or location. As discussed in connection with figures above, a dynamic tolerance curve may characterize an impact of a power quality event (or power quality events) in an electrical system. As also discussed in connection with figures above, in some embodiments at least one means for mitigating the impact of the power quality event (or power quality events) may be selected and applied in response to an analysis of the dynamic tolerance curve.

Subsequent to block 5120, the method 5100 may end in some embodiments. In other embodiments, the method 5100 may repeat again, for example, in response to a control signal or user input, or automatically to ensure that the dynamic tolerance curve associated with the third metering point or location is up-to-date.

In some embodiments, the electrical measurement data from or derived from energy-related signals captured by the at least one first IED may also be used to generate or update a dynamic tolerance curve associated with the first metering point. Additionally, in some embodiments the electrical measurement data from or derived from energy-related signals captured by the at least one second IED may also be used to generate or update a dynamic tolerance curve associated with the second metering point.

For example, in some embodiments the electrical measurement data from or derived from the energy-related signals captured by the at least one first IED in the electrical system may be processed to identify a power quality event at the first metering point, and to determine an impact of the identified power quality event at the first metering point. The identified power quality event and the determined impact of the identified power quality event at the first metering point may be used to generate or update the first dynamic tolerance curve associated with the first metering point. In some embodiments, the first dynamic tolerance curve characterizes at least an impact of power quality event(s) on the first metering point.

The at least one first IED may be configured to monitor one or more loads in the electrical system in some embodiments. In these embodiments, the first dynamic tolerance curve may further characterize a response of the one or more loads to the power quality events.

In some embodiments, the at least one second IED may not be configured to capture the power quality event, or the at least one second IED may be incapable of capturing the power quality event. In these embodiments, for example, an impact of the identified power quality event at the second metering point may be determined based on an evaluation of the electrical measurement data from or derived from the energy-related signals captured by the at least one second IED proximate to a time of occurrence of a power quality event identified at the first metering point. The time of occurrence of the identified power quality event at the first metering point may be determined, for example, by processing the electrical measurement data from or derived from the energy-related signals captured by the at least one first IED.

In some embodiments, the identified power quality event and the determined impact of the identified power quality event at the second metering point may be used to generate or update the second dynamic tolerance curve associated with the second metering point. In some embodiments, the second dynamic tolerance curve characterizes at least an impact of power quality event(s) on the second metering point.

The at least one second IED may be configured to monitor one or more loads in the electrical system in some embodiments. In these embodiments, the second dynamic tolerance curve may further characterize a response of the one or more loads to the power quality events.

In the above-described embodiments in which the at least one second IED may not be configured to capture the power quality event, or the at least one second IED may be incapable of capturing the power quality event, at least the determined time of occurrence of the identified power quality event at the first metering point may be communicated from the at least one first IED to at least one of: a cloud-based system, on-site software, a gateway, and another head-end system. The impact of the identified power quality event at the second metering point may be determined on the at least one of: the cloud-based system, the on-site software, the gateway, and the other head-end system in some embodiments.

In some embodiments, communicating the determined time of occurrence from the at least one first IED to the at least one of: the cloud-based system, the on-site software, the gateway, and the other head-end system, includes: producing at least one of a timestamp, alarm, and a trigger indicative of the determined time of occurrence on the at least one first IED; and communicating the at least one of the timestamp, the alarm, and the trigger to the at least one of: the cloud-based system, the on-site software, the gateway, and the other head-end system.

Referring to FIG. 52, a flowchart illustrates an example method 5200 for characterizing an impact of a power quality event on an electric system. The method 5200 may be implemented, for example, on a processor of at least one IED (e.g., 121, shown in FIG. 1A) and/or remote from the at least one IED in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 52, the method 5200 begins at block 5205, where energy-related signals (or waveforms) are measured and data is captured, collected, stored, etc. by at least one metering device in an electrical system. In some embodiments, the at least one metering device includes at least one of an IED and/or a virtual meter. Additionally, in some embodiments the energy-related signals include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages.

At block 5210, electrical measurement data from or derived from the energy-related signals captured by the at least one metering device at block 5205, is processed to identify a power quality event associated with at least one load (e.g., 111, shown in FIG. 1A) monitored by the at least one metering device. The at least one metering device and the at least one load are installed at respective locations in the electrical system.

At block 5215, it is determined if the identified power quality event has an impact on the at least one load or on the electrical system. If it is determined the identified power quality event has an impact on the at least one load or on the electrical system, the method proceeds to block 5220.

At block 5220, a recovery time for the at least one load or the electrical system to recover from the identified power quality event is determined. Additionally, at block 5225, the data captured, collected, and/or stored during the recovery time is tagged (or otherwise indicated) as atypical or abnormal based on the determined recovery time. In some embodiments, the recovery time is tagged (or otherwise indicated) in a dynamic tolerance curve associated with the at least one load or the electrical system.

Returning briefly now to block 5215, if it is determined the identified power quality event does not have an impact on the at least one load or on the electrical system, the dynamic tolerance curve may be updated and the method may end in some embodiments. Subsequent to block 5225, the method may also end in some embodiments.

In other embodiments, the method may include one or more additional steps. For example, in some embodiments in response to determining that the identified power quality event has an impact on the at least one load or on the electrical system, one or more metrics associated with the electrical measurement data may be compared against local utility rate structures to calculate a total energy-related cost of the identified power quality event, and to identify opportunities for reducing the total energy-related cost. It is understood that in some embodiments the total energy-related cost of the identified power quality event, and the identified opportunities for reducing the total energy-related cost are based on the tagged recovery time data.

Additionally, in some embodiments an economic impact of the identified power quality event may be determined based, at least in part, on one or more metrics associated with the determined recovery time. Example metrics are discussed throughout this disclosure.

Referring to FIG. 53, a flowchart illustrates an example method 5300 for reducing recovery time from a power quality event in an electrical system, for example, by tracking a response characteristic of the electrical system. The method 5300 may be implemented, for example, on a processor of at least one IED (e.g., 121, shown in FIG. 1A) and/or remote from the at least one IED in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 53, the method 5300 begins at block 5305, where energy-related signals (or waveforms) are measured and data is captured, collected, stored, etc. by at least one IED in an electrical system. In some embodiments, the energy-related signals include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages.

At block 5310, electrical measurement data from or derived from the energy-related signals captured by the at least one IED at block 5305, is processed to identify a power quality event associated with one or more portions of the electrical system.

At block 5315, at least one means for recovering from the identified power quality event is determined. Additionally, at block 5320 a selected one of the at least one means for recovering from the identified power quality event is applied.

At block 5325, a response characteristic of the electrical system is tracked in response to the selected one of the at least one means for recovering from the identified power quality event being applied. In some embodiments, the response characteristic of the electrical system is tracked with respect to a baseline response of the electrical system. In some embodiments, tracking the response characteristic includes identifying recurring event data. The identified recurring event data may be used, for example, to forecast power quality events in the electrical system.

As discussed above in connection with section VI of this disclosure, entitled “Disaggregation of Typical and Atypical Operational Data Using Recovery Time,” it is important to recognize a facility's operation during a recovery period is often aberrant or atypical as compared to non-recovery times (i.e., normal operation). Additionally, it is useful to identify and “tag” (i.e., denote) and differentiate aberrant or atypical operational data from normal operational data (i.e., non-recovery data) for performing calculations, metrics, analytics, statistical evaluations, and so forth. Metering/monitoring systems do not inherently differentiate aberrant operational data from normal operational data. Differentiating and tagging operational data as either aberrant (i.e., due to being in recovery mode) or normal provides several advantages, examples of which are provided in section VI of this disclosure.

At block 5330, the response characteristic of the electrical system is evaluated to determine effectiveness of the selected one of the at least one means for recovering from the identified power quality event. If it is determined that the selected one of the at least one means for recovering from the identified power quality event is not effective, the method may return to block 5315 in some embodiments. Upon returning to block 5315, at least one other means for recovering from the identified power quality event may be determined. Additionally, at block 5320 a selected one of the at least one other means for recovering from the identified power quality event may be applied.

Returning now to block 5330, if it is alternatively determined that the selected one of the at least one means for recovering from the identified power quality event is effective, the method may end in some embodiments. Alternatively, information about the issue and its resolution may be included in and/or appended to a history file, for example. In other embodiments, the method may return to block 5325 (e.g., such that the response characteristic of the electrical system may be further tracked).

As discussed in connection with figures above, an electrical system may include a plurality of metering points or locations, for example, metering points M₁, M₂, etc. shown in FIGS. 29, 30A, 30B, and 30E-33. In some embodiments, it may be desirable to generate, update or derive a dynamic tolerance curve for each metering point of the plurality of metering points. Additionally, in some embodiments it may be desirable to analyze power quality events in the electrical system, for example, based on analysis of data aggregated, extracted and/or derived from the dynamic tolerance curves.

Referring to FIG. 54, a flowchart illustrates an example method 5400 for analyzing power quality events in an electrical system. The method 5400 may be implemented, for example, on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system. In some embodiments, the at least one metering device may be located at a respective metering location of a plurality metering locations in the electrical system (e.g., M₁, shown in FIG. 29).

As illustrated in FIG. 54, the method 5400 begins at block 5405, where energy-related signals (or waveforms) are measured and data is captured, collected, stored, etc. by at least one of a plurality of metering devices in an electrical system. The at least one of a plurality of metering devices may each be associated with a respective metering location of a plurality of metering locations in the electrical system. For example, a first one of the plurality of metering devices may be associated with a first metering location in the electrical system (e.g., M₁, shown in FIG. 29), a second one of the plurality of metering devices may be associated with a second metering location in the electrical system (e.g., M₂, shown in FIG. 29), and a third one of the plurality of metering devices may be associated with a third metering location in the electrical system (e.g., M₃, shown in FIG. 29).

In some embodiments, the plurality of metering devices include at least one IED (e.g., 121, shown in FIG. 1A). Additionally, in some embodiments the energy-related signals measured by the plurality of metering devices include at least one of: voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages.

At block 5410, electrical measurement data from or derived from the energy-related signals captured by the plurality of metering devices at block 5405, is processed to generate, update and/or derive at least one of a plurality of dynamic tolerance curves using one or more of the techniques disclosed herein, e.g., in connection with FIGS. 45 and 51. In some embodiments, the at least one of a plurality of dynamic tolerance curves are generated, updated and/or derived for each of the plurality of metering locations in the electrical system. The plurality of metering locations include at least one physical metering point (e.g., M₁ and M₂, shown in FIG. 30F). In some embodiments, the plurality of metering locations may also include at least one virtual metering point (e.g., V₁, shown in FIG. 30F).

Similar to the dynamic tolerance curves discussed throughout this disclosure, each of the plurality of dynamic tolerance curves generated, updated and/or derived at block 5410 may characterize and/or depict a response characteristic of the electrical system. More particularly, each of the plurality of dynamic tolerance curves may characterize and/or depict a response characteristic of the electrical system at a respective metering point of the plurality of metering points in the electrical system (e.g., M₁, M₂, etc., shown in FIG. 29). In embodiments in which the plurality of metering points include at least one virtual metering point, the dynamic tolerance curve for the at least one virtual metering point may be derived from the dynamic tolerance curve data for at least one physical metering point, for example, as discussed above in connection with FIG. 30F and other figures herein.

In some embodiments, one or more of the plurality of dynamic tolerance curves may have associated thresholds or setpoints, for example, for triggering alarms in the electrical system. The thresholds or setpoints may include one or more upper alarm thresholds and/or one or more lower alarm thresholds in some embodiments, several examples of which are described above in connection with FIGS. 49 and 50, for example. In accordance with various aspects of this disclosure, an alarm may be triggered in response to a power quality event being above one or more upper alarm thresholds or below or below one or more lower alarm thresholds. Additionally, in accordance with various aspects of this disclosure, an action (or actions) may be taken in response to the alarm (or alarms) being triggered.

Example actions that may be performed in response to the alarm being triggered, e.g., due to an anomalous voltage condition, may include, for example, reporting an anomalous voltage condition (e.g., through a voltage event alarm generated by at least one IED) and/or automatically performing an action (or actions) affecting at least one component of the electrical system, e.g., starting a diesel generator, operating a throw-over or static switch, etc. Similar to method 4500 described above in connection with FIG. 45, in some embodiments a control signal may be generated in response to the anomalous voltage condition, and the control signal may be used to affect the at least one component of the electrical system (e.g., a load monitored by the at least one IED, or a mitigative device such as a diesel generator, throw-over or static switch, etc.). The control signal may be generated by the at least one IED, a control system, or another device, software or system associated with the electrical system. As discussed in figures above, in some embodiments the at least one IED may include or correspond to the control system. Additionally, in some embodiments the control system may include the at least one IED.

Further example actions that may be performed in response to the alarm being triggered, e.g., to prevent a potential tripping due to a voltage sag duration or magnitude, may include, for example, turning “off” some running process steps which are identified as potential “sag exacerbators” such as postponing (if possible) a motor start during an existing voltage sag. Additionally, in some embodiments a user alarm (e.g., sent to a mobile phone application or other device application) may be triggered in response to the alarm (i.e., a system alarm) being triggered. Further, in some embodiments the alarm may be used as input to a manufacturing SCADA system, for example, to change or delay another process. The alarm may also be remedial, for example, by providing the maintenance team with a precise localization of an issue and a priority list of what issue(s) is/are most critical, which issue(s) to address first, second, etc. In some embodiments, the system may also provide a recommended response (or responses) to the issues, e.g., for correcting the issues. One example recommended response may be to disconnect and/or reconfigure a load causing the issues, with the disconnecting and/or reconfiguring being manually performed by the user and/or automatically performed by the system. As illustrated above, the types of actions that can be performed in response to alarms can take a variety of forms.

The above-discussed thresholds or setpoints (i.e., thresholds for triggering alarms) may be generated during a so-called “learning period” in some embodiments, as discussed further below in connection with FIG. 55, for example.

In accordance with some aspects of this disclosure, the dynamic tolerance curves generated, updated and/or derived at 5410 can be prepared by deducing relevant signal characteristics from the above-discussed electrical measurement data. Examples of relevant signal characteristics (and information) may include severity (magnitude), duration, power quality type (e.g., sag, swell, interruption, oscillatory transient, impulsive transient, etc.), time of occurrence, process(es) involved, location, devices impacted, relative or absolute impact, recovery time, periodicity of events or event types, etc. Additional examples of relevant signal characteristics may include identified changes in current, phase shifts, etc., which result in an interpretation (or interpretations) such as “inductive or capacitive load type added or removed from system,” “percentage of pre-event loads added or dropped from system” during an event, etc. In some embodiments, these relevant signal characteristics are determined from portions of the electrical measurement data from prior to a start time of power quality events, and from portions of the electrical measurement data from after an end time of power quality events. Additionally, in some embodiments these relevant signal characteristics are determined from portions of the electrical measurement data from during the power quality events (such as magnitude, duration, phases impacted, etc.).

In some embodiments, at least one of the plurality of dynamic tolerance curves may be displayed on a graphical user interface (GUI) (e.g., 230, shown in FIG. 1B) of at least one metering device, and/or or a GUI of a control system associated with the electrical system, for example. In some embodiments, the at least one of the plurality of dynamic tolerance curves is displayed in response to user input. The control system may correspond to or include a power or industrial/manufacturing SCADA system, a building management system, and a power monitoring system, as a few examples.

In some embodiments, the method may proceed to block 5415 after block 5410. In other embodiments, the method may return to block 5405 and blocks 5405 and 5410 may be repeated, for example, for capturing additional energy-related signals and updating the plurality of dynamic tolerance curves generated at block 5410. In doing so, the plurality of dynamic tolerance curves may be automatically/dynamically maintained at their optimal level. It is understood that optimal may mean different things due to the nature of the loads, segments, processes, and/or other inputs. This may result in a multitude of different curves, each or all relevant to different usages (applications). According to some aspects of this disclosure, “dynamic” of the dynamic tolerance curve means that there are changes which may occur over time. Thus, time may be an important factor to model, or an important factor to include in any model or curve. This is core to the innovation at each meter, and then at the global system level analysis, aggregation, action recommendation or any related automation changes or commands (non-exhaustive list). Thus, a model can be built of the evolution over time. To simplify the understanding, this may be represented by two (or more) different linked graphs, for example. A trend of each change point's optimal values (what is the value of the “initial impact” point). Additionally, the relative impact on loads or critical loads, such as the percentages of loads dropping off at an initial impact point, over time.

At block 5415, power quality data from the plurality of dynamic tolerance curves is selectively aggregated. In some embodiments, the power quality data is selectively aggregated based on locations of the plurality of metering points in the electrical system. For example, power quality data from dynamic tolerance curves associated with metering points in a same or similar portion of the electrical system may be aggregated.

Additionally, in some embodiments the power quality data is selectively aggregated based on criticality or sensitivity of the plurality of metering points to power quality events. For example, power quality data from dynamic tolerance curves associated with highly critical or sensitive metering points may be aggregated. In some embodiments, the criticality of the metering points is based on the load type(s) at the metering points, and the importance of the loads to operation of a process (or processes) associated with the electrical system. Additionally, in some embodiments the sensitivity of the metering points is based on the sensitivity of the loads to power quality events.

In accordance with some aspects of this disclosure, degradations or improvements in the loads or the electrical system's sensitivity or resilience to power quality events may be identified at each respective metering point in the electrical system. The degradations or improvements in the electrical system's sensitivity or resilience to power quality events may be identified, for example, based identified changes in the dynamic tolerance curves for each respective metering point. In some embodiments, the identified degradations or improvements in the loads or the electrical system's sensitivity or resilience to power quality events may be reported. For example, the identified degradations or improvements in the loads or the electrical system's sensitivity or resilience to power quality events may be reported by generating and/or initiating a warning (e.g., a key performance indicator) indicating the identified degradations or improvements in the electrical system's sensitivity or resilience to power quality events. The warning may be communicated via at least one of: a report, a text, an email, audibly, and an interface of a screen/display, for example. In some embodiments, the screen/display may correspond to a screen/display on which the dynamic tolerance curve(s) is/are presented. The communicated warning may provide actionable recommendations for responding to the identified degradations or improvements in the electrical system's sensitivity or resilience to power quality events.

In accordance with further aspects of this disclosure, power quality data from the plurality of dynamic tolerance curves may be selectively aggregated to generate one or more aggregate dynamic tolerance curves.

In some embodiments, the plurality of dynamic tolerance curves may be aggregated by graphing the plurality of dynamic tolerance curves in a composite plot (or plots). For example, the response characteristic of the electrical system at each metering point, e.g., as indicated in the plurality of tolerance curves, may be presented as a sub-graph in the composite plot (or plots). In some embodiments, the plurality of tolerance curves may be presented in the composite plot (or plots), e.g., in order of appearance in the plot (or plots), based on a criticality or sensitivity ranking (1^(st) to last) of the metering points. It is understood that other groupings may be overlaid, for example, to reflect an analysis by location. One may first group by location such as one graph per location, then sort the meters in decreasing order of sensitivity or criticality.

In accordance with some aspects, it is possible to overlay common impact model(s) and highlight key specificities of each meter by color-coding, for example. A dynamic tolerance curve generated for a most sensitive high-criticality (e.g., on magnitude initial start) meter in an electrical system may, for example, serve as a reference curve for each and all the other meters. Then it is possible to play on other graphical components to highlight the criticality of a load/process if tagged for each or certain meters (for example by using the line thickness, shading, or coloring the curve line in red, green or blue for “highly-critical”, “somewhat-critical”, “not-critical”, etc.).

Additionally, in accordance with some aspects, dynamic tolerance curves associated with each relevant group of meters may be presented as a sub-graph in the composite plot (or plots). The relevance of a group may depend, for example, on the goal (or goals) of the composite plot (or plots). It is understood that many different relevant groupings may be used in different contexts. For example, the curves/meters may be grouped for location (e.g., meter location) based analysis. Additionally, the curves/meters may be grouped by criticality. For example, highly critical meters may be grouped vs. the other categories of meters. Additionally, all the individual highly-critical meters may be plotted against an aggregated curve of less critical meters. The curves/meters may also be grouped by impact, e.g., group meters by similar percentages at a given magnitude of a voltage sag and duration level. Additionally, the meters may be grouped by sensitivity curve or range, e.g., distance between initial and final impact.

It is understood that the above criteria may be combined. For example, a filter may be applied per location, so a grouping by location. Then the typical location may be plotted where all meters at this location are used to calculate the mean thresholds and change points. Aggregated typical graphs of highly critical meters may then be compared with the minimal “initial impact values” curve from the typical (mean) “initial impact values” curve. This would indicate whether there is one or a few IEDs creating the “weak points” of the system that may need to be made more robust (e.g., SagFigher® solution to be analyzed).

The ranking (first to last) in order of appearance of each meter may be based on the criticality or the sensitivity of the electrical system and/or the load (or loads) at the metering point(s) associated with the meter, for example. However, other groupings may be overlaid. For example, to reflect an analysis by location, one may first group the meters by location, then sort the meters in decreasing order of sensitivity or criticality.

Subsequent to block 5415, the method may proceed to block 5420. At block 5420, power quality events in the electrical system are analyzed based on the selectively aggregated power quality data. For example, the selectively aggregated power quality data may be processed or analyzed to determine a relative criticality score of each of the power quality events to a process or an application associated with the electrical system. The relative criticality score may be based on an impact of the power quality events to the process or the application, for example. For example, a rooftop HVAC unit or system dropping off for three hours for an office building may be considered less critical then for a data center. Additionally, a motor in a steel production line may be considered more critical than a lighting rail or fixture above the steel production line. In some embodiments, the relative criticality score may be used as an input for supervised learning. Supervised learning means that some variable(s) may be used to teach the calculation engine which issues have more value than others.

In some embodiments, the impact of the power quality events is related to tangible or intangible costs associated with the power quality events to the process or the application. Additionally, in some embodiments the impact of the power quality events is related to relative impact on loads in the electrical system. In some embodiments, there may be an absolute threshold or non-acceptable trip off-line of a load (or loads) related to the process or the application. For example, in the above example of a data center, a cooling pump of the HVAC system may be considered a critical component, which should never be trip off-line. Additionally, in the above example of a steel production line, there may be a path in the production line where there is no redundancy of motor on the conveyor belt. This may be the component or process which should never misoperate, trip off-line, and/or malfunction.

In some embodiments, the determined relative criticality score, e.g., at block 5420, may be used to prioritize responding to (or a response to) the power quality events. For example, the determined relative criticality score (e.g., a criticality score using a historical analysis of the voltage event impacts) may be used to determine how significant load loss effects the system operation. The more significant the impact, the worse the relative criticality score as related to other IEDs in the system. This could also be performed based on discrete locations of said IEDs (i.e., some locations are more significantly impacted by voltage events than other locations). Some locations may inherently have worse criticality scores than others due to their processes, functions, and/or loads.

The relative criticality score may also be used, for example, as an input to drive a manufacturing SCADA to determine if an alternative process should be triggered or terminated. For example, voltage sags may result in poorer product quality or product defects such as fluctuations in the heat and pressure conditions producing an increase in the quantity of air bubbles in rubber products. A higher criticality score may reflect these portions of the production process. In this case, an alternative/corrective process may be triggered to complement the current process step or scrap the product.

In embodiments in which the power quality data from the plurality of dynamic tolerance curves has been selectively aggregated to generate one or more aggregate dynamic tolerance curves at block 5415, the aggregate dynamic tolerance curves may be analyzed or otherwise evaluated to analyze power quality events in the electrical system.

The power quality events analyzed at block 5420 may include, for example, at least one of: a voltage sag, a voltage swell, a voltage transient, an instantaneous interruption, a momentary interruption, a temporary interruption, and a long-duration root-mean-square (rms) variation.

In some embodiments, at block 5420 it may also be determined if any discrepancies exist between the selectively aggregated power quality data. For example, the discrepancies may include inconsistent naming conventions in the selectively aggregated data. As one example, the naming conventions for power quality data in a dynamic tolerance curve associated with a first metering point in the electrical system may be different from the naming conventions for power quality data in a dynamic tolerance curve associated with a second metering point in the electrical system. In embodiments in which discrepancies are identified, additional information may be requested (e.g., from a system user) to reconcile the discrepancies. Recommendations may be made.

After block 5420, the method may end in some embodiments. In other embodiments, the method may return to block 5405 and repeat again (e.g., for capturing additional energy-related signals, updating the dynamic tolerance curves associated with the metering points in the electrical system, and selectively aggregating power quality data from the updated dynamic tolerance curves to analyze power quality events).

It is understood that method 5400 may include one or more additional blocks in some embodiments. For example, the method 5400 may include tagging power quality events in one or more of the plurality of dynamic tolerance curves with relevant and characterizing information, for example, based on information extracted about the power quality events. In some embodiments, the relevant and characterizing information includes at least one of: a severity score, recovery time, percentage of loads added or lost during event, inductive or capacitive load(s) added or dropped during event, location within a system, type of load or loads downstream from the IED providing the information, impact to a facility's operation, etc. Additionally, in some embodiments the information about the power quality events may be extracted from portions of electrical measurement data from energy-related signals captured prior to a start time of the power quality events. In some embodiments, the information about the power quality events may be extracted from portions of electrical measurement data from energy-related signals captured after an end time of the power quality events. Information may also be extracted from portions of electrical measurement data from energy-related signal captured during the power quality events.

In accordance with some aspects of this disclosure, tagging and data enriching can be performed at substantially any time. For example, tagging and data enriching may be performed before or during the site commissioning based on the loads which will be running, and based on calculations and process planning (times of preparation and of calculations) as a few examples. During normal operations, events may be tagged on the fly (times of normal running operations). Additionally, events may be tagged during re-commissioning or extensions, or maintenance (e.g., times of changes).

As discussed in connection with figures above, a dynamic tolerance curve may characterize an impact of a power quality event (or power quality events) in an electrical system. As also discussed in connection with figures above, in some embodiments at least one means for mitigating the impact of the power quality event (or power quality events) may be selected and applied in response to an analysis of the dynamic tolerance curve. For example, in accordance with some aspects of this disclosure, the dynamic tolerance curve may be used to prevent tripping of more critical loads by driving counter measures, including system level process optimization. For illustration purposes, if a user specifies absolute thresholds, the system (e.g., control system) may calculate an optimal curve to avoid the risk of tripping (the protection relay) for this given meter. Additionally, the system may decide to turn off typical loads which are known (or calculated) as possible causes of a power quality event (e.g., voltage sags), or cut first (faster) other potential process steps which may generate risks of increasing or maintaining longer duration of the power quality event.

It is understood that one or more steps of method 5400 may be combined with one or more steps of other methods discussed throughout this disclosure, for example, for generating, updating and/or deriving dynamic tolerance curves.

Referring to FIG. 55, a flowchart illustrates an example method 5500 for generating dynamic tolerance curves in accordance with embodiments of this disclosure. In accordance with some embodiments of this disclosure, method 5500 is illustrative of examples steps that may be performed at blocks 5405 and 5410 of method 5400 discussed above in connection with FIG. 54. Similar to method 5400, method 5500 may be implemented on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 55, the method 5500 begins at block 5505, where a so-called “learning period” begins. At block 5510, energy-related signals (or waveforms) are measured and data is captured, collected, stored, etc. by a plurality of metering devices in an electrical system. In some embodiments, the data captured corresponds to measurement data required for the calculation of thresholds or setpoints at block 5515.

At block 5515, thresholds or setpoints (e.g., alarm thresholds) are calculated based on the energy-related signals measured at block 5510. Additionally, in accordance with some embodiments of this disclosure, anticipatory thresholds (e.g., preventive thresholds) may be determined at block 5515, e.g., as a percentage (e.g., 90%) of the regular thresholds to prevent damage to electrical system loads. Critical and non-critical loads may have different anticipatory thresholds, for example, based on their impact on the electrical system. For critical or more sensitive loads, the anticipatory thresholds may be tighter or closer to the nominal voltage. Additionally, for non-critical or less sensitive loads, the anticipatory thresholds may be looser or father from the nominal voltage. For example, anticipatory thresholds may be set between a sag alarm threshold of ±10% of the nominal voltage and the “initial impact voltage threshold” (also called Initial impact point) for some “critical loads,” e.g., for a voltage sag for instance. Additionally, for some “non-critical loads,” the anticipatory thresholds may be set to 30% of loads lost, e.g., for a voltage sag. Example threshold calculations for individual meters in an electrical system are shown in the chart on FIG. 73.

Additionally, example threshold calculations for groups of meters in an electrical system, for example, based on criticality groups, are shown in the chart on FIG. 74.

As noted and illustrated above, meters may be grouped based on criticality. For example, the meters may be grouped in “high,” “medium” and “low” criticality groups, e.g., based on whether the meters include, or are coupled to, critical or non-critical loads. Meters grouped in the high criticality group may have thresholds selected, for example, to prevent voltage sags. Additionally, meters grouped in the medium criticality group may have thresholds selected, for example, to trigger process changes. Further, meters grouped in the low criticality group may have thresholds selected, for example, such that HVAC capacity loss creates only over consumption and drop of comfort ° C. An example method for moving from the individual meters thresholds calculation to the groups of meters thresholds calculation shown in the charts above is illustrated in FIG. 55A, for example.

In accordance with embodiments of this disclosure, the above-discussed thresholds may be adjusted (either manually, automatically, or semi-automatically). For example, the thresholds may be manually adjusted in response to usage and/or user/process impact analysis, e.g., by type of load/process, as illustrated in the chart on FIG. 75.

Additionally, the thresholds may be automatically adjusted, for example, in response to recovery duration, as illustrated in the chart on FIG. 76.

Referring now to block 5520, at block 5520 it is determined if the learning period should be continued. For example, in some embodiments the learning period may have an associated (e.g., pre-programmed or user configured) time period, and once a time associated with the learning period reaches the associated time period it may be determined that there is no need to continue the learning period. In other embodiments, it may be determined if “sufficient” data has been obtained to generate the thresholds or setpoints, and once sufficient data has been obtained it may be determined that the there is no need to continue the learning period.

In some embodiments, during the learning period, events, process parts, or meters in an electrical system may be tagged, for example, based on measurement data collected during the learning period, e.g., at block 5510.

If it is determined that the learning period should be continued, the method returns to block 5510, and blocks 5510, 5515 and 5520 are repeated. Alternatively, if it is determined that there is no need to continue the learning period, the method proceeds to block 5525.

At block 5525, which is similar to block 5405 of method 5400 in some embodiments, energy-related signals (or waveforms) are measured and data is captured, collected, stored, etc. by at least one of a plurality of metering devices (i.e., IEDs) in an electrical system. Additionally, at block 5530, which is similar to block 5410 of method 5400 in some embodiments, electrical measurement data from or derived from the energy-related signals captured by the plurality of metering devices at block 5525, is processed to generate, update and/or derive at least one of a plurality of dynamic tolerance curves. In some embodiments, the at least one of a plurality of dynamic tolerance curves are generated, updated and/or derived for each of a plurality of metering points in the electrical system.

After block 5525, the method may end in some embodiments. In other embodiments, the method may return to block 5525 and repeat again (e.g., for capturing additional energy-related signals, and updating the dynamic tolerance curves associated with the metering points in the electrical system). In further embodiments, the method may return to block 5505 and repeat again. More particularly, in accordance with some aspects, the learning period may be initiated again, for example, from time to time, in response to user input, etc.

Similar to method 5400, it is understood that method 5500 may include one or more additional blocks in some embodiments. For example, in some embodiments the thresholds or setpoints calculated at block 5515 may be defined or redefined after the above-discussed learning period. Additionally, similar to method 5400, it is understood that one or more steps of method 5500 may be combined with one or more steps of other methods discussed throughout this disclosure, for example, for calculating the thresholds or setpoints and/or generating, updating and/or deriving dynamic tolerance curves.

Referring to FIG. 56, a flowchart illustrates an example method 5600 for tagging criticality scores, for example, during a learning period, such as the learning period discussed above in connection with FIG. 55. Similar to method 5500, method 5600 may be implemented on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 56, the method 5600 begins at block 5605, where criticality scores are tagged for each metering device in an electrical system. As discussed through this disclosure, each metering device (e.g., IED) in an electrical system may be associated with a respective metering point in the electrical system, and electrical measurement data from energy-related signals captured by each metering device may be used to generate a dynamic tolerance curve associated with a respective metering point. As also previously discussed, absolute or relative criticality scores may be determined for metering devices, new power quality events, etc. In accordance with some aspects of this disclosure, a user may tag criticality scores for each metering device in an electrical system at block 5605. In accordance with other aspects of this disclosure, a system (e.g., a system for analyzing power quality events) may tag criticality scores for each metering device in an electrical system at block 5605.

At block 5610, criticality scores for each new power quality event may be tagged, in some embodiments by the system, and in other embodiments by the user. Each new power quality event may be identified, for example, using one or more techniques discussed in connection with figures above (e.g., method 4500, shown in FIG. 45). At block 5615, information tags may be added for each new power quality event or criticality score (such as criticality of a specific industrial or building process running at event time). For example, tagged information (i.e., metadata, real data, etc.) may be appended/update for each new power quality event. In short, new information may be optionally appended to a device, process, and/or system as events occur. Criticality scores may also be changed and/or update at device(s), process(es), and/or system(s) as relevant new data comes in. In some embodiments, these information tags may correspond to system added or inferred information tags. The system added or inferred information tags may, for example, help to identify co-occurrences, possible sources, etc., such as power factor changes, source changed identification, inductive or resistive loads added or lost, capacitor bank switched “on” or “off,” and/or metadata from building management system or SCADA system, for example. Additionally, the system added or inferred informational tags may indicate correlations between power quality events associated with different metering devices in an electrical system. The correlations being deduced, for example, from analysis of the power quality events using one or more techniques previously disclosed herein.

After block 5615, the method may end in some embodiments. In other embodiments, the method may return to block 5605 and repeat again (e.g., for tagging criticality scores for further new events, and/or updating criticality scores for metering devices). In further embodiments, the method may include one or more additional steps, as will be appreciated by one of ordinary skill in the art.

Referring to FIG. 57, a flowchart illustrates another example method 5700 for tagging criticality scores, for example, during a learning period, such as the learning period discussed above in connection with FIG. 55. Similar to method 5500, method 5700 may be implemented on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system. In accordance with some aspects, method 5700 corresponds to data driven system tagging of criticality scores per phase for each new power quality event.

As illustrated in FIG. 57, the method 5700 begins at block 5705, where a percentage of loads lost in response to each new event is determined. As discussed in connection with figures above, an electrical system may include one or more loads, and a metering device (e.g., an IED) may be configured to monitor at least one of the one or more loads. In some embodiments, the percentage of loads lost may be determined by evaluating new initial impact thresholds and/or new final impact thresholds, and positions in between these thresholds (e.g., distribution or function approximation), e.g., to interpolate a voltage event's impact using different models. These different models, as illustrated in FIGS. 57A-57K, for example, as will be discussed further below, may be used to approximate how the electrical system responds to each new power quality event.

In accordance with some embodiments, initial impact thresholds may correspond to thresholds for detecting a first percentage of loads lost. For example, one illustrative initial impact threshold or point may correspond to a threshold of detecting 20% of loads lost at 75% of nominal voltage, e.g., for a specific time duration. In the above example, the 20% of loads lost corresponds to the first percentage of loads lost, and the initial impact point, as illustrated in FIG. 57A, for example.

In accordance with some embodiments, final impact thresholds may correspond to thresholds for detecting when all loads (i.e., 100% of the loads) are lost. For example, one illustrative final impact threshold or point may correspond to a threshold of detecting 100% of loads lost at 55% of nominal voltage, e.g., for a specific time duration. In the above example, the 100% of loads lost corresponds to the final impact point, as illustrated in FIG. 57A, for example.

Positions (or zones) in between the above-discussed initial impact thresholds and final impact thresholds may be determined, for example, using one or more models, as briefly discussed above. In accordance with one example linear model, as shown in FIGS. 57B and 57C, for example, between 75% and 55% of the nominal voltage, 4% of load loss for each 1% decrease in the nominal voltage. Additionally, in accordance with one example logarithmic (or log) model, as shown in FIGS. 57B-57D, for example, between 75% and 55% of the nominal voltage, the log percentage of load loss for each 1% decrease in the nominal voltage.

In accordance with some embodiments, these inferred “curve model” functions can be determined by substantially any approximated or modeled line or curve calculation or curve fitting algorithm. Relevant models can be calculated and applied as a maximum function, as illustrated in FIGS. 57B and 57C, for example, where the curve represents the “normal” curve. It is understood that other statistical and inferred model types may be included in this approach, such as, for example, mean, median, min, loss, regression, and other calculations.

In applying these functions, such as illustrated in FIGS. 57C and 57D and the table provided directly beneath this paragraph, the form of the curve(s) may change due to an increasing stepwise function overlaid to the “curve model” function. This is typical and reflects the closer an IED is to the terminus of the electrical system, the more distinct the load changes will be (here, maybe five different motors). Conversely, the closer an IED is to the source (e.g., utility delivery point), the less distinct load changes will typically be. Thus, the load profile may appear to be more “continuous” due to the greater diversity of the downstream loads in many cases, such as in our examples, there may be ten (or any number of) different motors.

Linear Linear Log Log few many few many Linear loads loads Log loads loads Magnitude continous steps steps continous steps steps 75 20 20 20 20 20 20 74 24 20 20 45 20 20 73 28 20 28 58 20 58 72 32 20 28 65 20 58 71 36 20 36 71 20 71 70 40 40 36 75 75 71 69 44 40 44 78 75 78 68 48 40 44 81 75 78 67 52 40 52 83 75 83 66 56 40 52 85 75 83 65 60 60 60 87 87 87 64 64 60 60 89 87 87 63 68 60 68 91 87 91 62 72 60 68 92 87 91 61 76 60 76 93 87 93 60 80 80 76 95 95 93 59 84 80 84 96 95 96 58 88 80 84 97 95 96 57 92 80 92 98 95 98 56 96 80 92 99 95 98 55 100 100 100 100 100 100

It is understood the loads (e.g., motors) may be different sizes and have various response characteristics. In the example given, larger loads may be much more sensitive to relays tripping (i.e., due to voltage sags) than smaller loads. This would explain a log function such as provided in the above-discussed example.

It is also understood that the above-discussed models and functions may be based on, or adjusted based on, power quality event sensitivity. For example, as illustrated in FIGS. 57E and 57F, the loss of loads curves may be modeled based on event maximum sensitivity. Additionally, as illustrated in FIGS. 57G and 57H, the loss of loads curves may be modeled based on event median sensitivity. Outliers from the curves may be identified as most oversensitive, for example, as shown in FIG. 571. In some embodiments, the outliers may be identified as first candidates for correlation analysis, for example, to determine if they accurately reflect how the electrical system responds to power quality events. Referring briefly to FIG. 57J, it is understood that in some embodiments the above discussed thresholds (e.g., initial impact and final impact thresholds) may be used to identify 50% (or another percentage) of loads lost. Also referring briefly to FIG. 57K, curves for a plurality of meters (e.g., three meters) may be aggregated, for example, using the most sensitive meter of the plurality of meters.

Returning now to FIG. 57, at block 5710 of method 5700 the severity of each new power quality event may be determined. In some embodiments, the severity is indicated in the form of a severity score which may be based, for example, on a magnitude and a duration of the power quality event. The “severity score” could be based on relative values (percentage of total load on any particular device, including the device measuring the voltage event) or absolute values (based on real measured value in kW, kVA, and/or other parameter), with the duration corresponding to how long the event lasted. One example way to calculate the severity score includes combining the magnitude of the power quality event with the duration of the power quality event, i.e., the severity score is a product of the magnitude and the duration. For example, the duration (e.g., real impact duration) of the power quality event may be combined with (e.g., multiplied by) the magnitude of voltage and/or current measurements from prior to, during and/or after the power quality event to determine the severity score. It is understood that there may be a number of other ways in which the severity score may be determined. For example, the severity score may be determined by or derived from at least one of power quality event magnitude, duration, recovery time, percentage of recovery loss, power quality event location, etc. It is understood that different zones in an electrical system may have higher priority than other-zones. Additionally, it is understood that other scores may be determined in addition to the severity score, for example, percentage of load loss in electrical system in response to the power quality event. In some embodiments, the percentages of load loss may be used to calculate the severity.

At block 5715, a recovery time is determined for the system to recover (e.g., get back to normal, or close to normal) from each new power quality event, for example, using techniques discussed in connection with figures above (e.g., FIG. 53).

At block 5720, information tags for each new quality event are added. In some embodiments, these information tags may correspond to system added or inferred information tags, as discussed above.

After block 5720, the method may end in some embodiments. In other embodiments, the method may return to block 5705 and repeat again (e.g., for additional tagging). In further embodiments, the method may include one or more additional steps, as will be appreciated by one of ordinary skill in the art.

Referring to FIG. 58, a flowchart illustrates an example method 5800 for generating dynamic tolerance curves, for example, after a learning period, such as the learning period discussed above in connection with FIG. 55. In accordance with some aspects, each metering location's dynamic tolerance curve may be modeled after the learning, as described above in connection with FIGS. 34-40, and as will be appreciated from further discussions below. Similar to method 5500, method 5800 may be implemented on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 58, the method 5800 begins at block 5805, where phases for each new power quality event in an electrical system are calculated. For example, the phases for each new event may be reconciled by calculating the aggregated phases for each new power quality event (mean, max, min, etc., resulting in “all phases mean,” “worst phase,” “non-impacted phase,” etc.). In accordance with some aspects of this disclosure, the phases refer to the phases of a three-phase system here. A power quality event may originate on any one, two or all three phases; however, it can move to (involve) any other phases as the power quality event evolves. Aggregating the phases include evaluating the power quality event from any one or two phases with respect to all three phases in the system. This approach can also be employed for single-phase systems with multiple legs (e.g., a typical house). In accordance with some aspects of this disclosure, the electrical system may be viewed through the lens of the three phases providing energy to said electrical system. Aggregations (and dynamic tolerance curves) may occur on each phase, independent of the other two phases. So, each phase in a three-phase system may be shown in its own dynamic tolerance curve, whether for a single metering location or from a system perspective. For example, producing a dynamic tolerance curve for Phase ‘A’ at a specific metering location or producing a dynamic tolerance curve for Phase ‘A’ that incorporates more than one metering device in the electrical system. These “devices” may also include virtual meters as well.

At block 5810, initial impact dynamic tolerance curves are generated or derived for each metering location in the electrical system. Additionally, at block 5815 final impact dynamic tolerance curves are generated or derived for each metering location in the electrical system. In some embodiments, the initial impact dynamic tolerance curves correspond to dynamic tolerance curves generated or derived from electrical measurement data captured by a metering device at each metering location at a first, initial time. Additionally, in some embodiments the final impact dynamic tolerance curves correspond to dynamic tolerance curves generated or derived from electrical measurement data captured by a metering device at each metering location at a second time after the first time. Both the initial impact dynamic tolerance curves and the final impact dynamic tolerance curves may indicate an impact of power quality events at the metering points in the electrical system.

At block 5820, the function of sensitivity of the dynamic tolerance curves may be calculated or approximated. In some embodiments, the function of sensitivity may be calculated or approximated based on the progression of load loss as voltage drops progressively, between initial impact and final impact.

At block 5825, acceptability thresholds for each metering point or location may be defined or inferred from tags (e.g., system or user tags) in the dynamic tolerance curves. For example, “no impact acceptable” may equate to “initial impact dynamic tolerance curve,” but maybe 30% of load losses are deemed acceptable for a specific meter, while other meters may have a threshold at 10% acceptability. In accordance with some aspects of this disclosure, in the vast majority of cases the loss of a load is either “acceptable” or “not acceptable.” When a meter is monitoring multiple loads that encompass both “acceptable” and “not acceptable” conditions, for example, this combination of “acceptable” and “not acceptable” impacts may be what leads to unique thresholds at discrete metering locations.

At block 5830, which is optional in some embodiments, other systems (such as for example BMS, power SCADA, manufacturing SCADA) may add new criticality scores (especially if no user or system tagging). In the case of a manufacturing site with an industrial SCADA system, for example, a criticality score may be calculated based on the load loss, e.g., with no additional user inputs. In some embodiments, the criticality score may be inferred for each meter from the load loss. For example, “no load loss” may correspond to a criticality score of 0, “20% initial load loss” may correspond to a criticality score of 20, and “30% initial load loss” may correspond to a criticality score of 30, and so forth. In some embodiments, the industrial SCADA system may be linked with a Power SCADA, and some industrial processes may be mapped with some of loads and meters in the electrical system. When measuring voltage sags, for example, meters responsible for stopping critical processes in the electrical system may be identified. This information may be added and fed into the Power SCADA, and the criticality scores for these meters may be updated (e.g., from 0 to 100).

At block 5835, criticality modeled dynamic tolerance curves are generated. For example, criticality tags may be modeled by the system using expert rules or feeding them into supervised learning algorithms or other artificial intelligence (AI) tools to build a criticality model for each meter or metering point. In some embodiments, the criticality model may reconcile potential discrepancies event by event and across all events of the meter, of user tags and other system tags. The output is thus a criticality modeled dynamic tolerance curve (or curves). In accordance with some embodiments, the criticality modeled dynamic tolerance curve(s) may be generated using simple rules, such as above, or other more complex calculations, for example, in response to one or more user inputs. For example, the impact of a voltage sag on some production quality metrics may be monitored to determine how voltage sags impact the process (e.g., increase from 2 to 100 defective parts per million (ppm)). In parallel a user may tag the IEDs. IEDs that are linked to the production may correct the original user criticality scores by new “ppm-related scores.” In some embodiments, “ppm” may be applied instead of “load loss” as a dynamic tolerance curve criticality calculation.

After block 5835, the method may end in some embodiments. In other embodiments, the method may include one or more additional steps, as will be appreciated by one of ordinary skill in the art.

Referring to FIG. 59, a flowchart illustrates an example method 5900 for leveraging and combining dynamic tolerance curves. Similar to method 5800, method 5900 may be implemented on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As illustrated in FIG. 59, the method 5900 begins at block 5905, where meter (or metering device) rankings and groups are determined or defined. As discussed above, each metering device (e.g., IED, virtual, etc.) in an electrical system may be associated with a respective metering location (physical or virtual) in the electrical system. In some embodiments, the meter rankings and groups are defined using the different meter criticalities defined for each meter based on meter criticality scores (when available). Additionally, in some embodiments meter rankings and groups may be defined leveraging the relative acceptability levels of different event tags (or non-acceptability). Manual configuration is also possible.

Example rankings according to the disclosure include, but are not limited to:

-   -   “Initial impact” ranking based on magnitude values, e.g., start         with most sensitive to initial impact (e.g., the number of loads         dropped) and rank metering devices starting at 80% of nominal         voltage until, in one example, the last metering device, where         the initial impact only starts at 45% of nominal voltage.     -   “Final impact” ranking similar to the “initial impact” ranking,         except the magnitude of the final impact is used to rank the         metering devices.     -   For a “significant load loss” predetermined values (e.g., 25% of         load loss), the magnitude of the load loss may be used to rank         the metering devices.     -   “Criticality score” related ranking may be another possible         ranking.     -   “Size of loads” related ranking, e.g., using the maximum energy         consumption and ranking in descending order.

Example groupings according to the disclosure include, but are not limited to:

-   -   Per load type (e.g., HVAC, motors, lighting, etc.).     -   Per physical or geographic location (per campus sector, per         building, per floor, per zone, etc.).     -   Per electric hierarchical branch or level (e.g., main meters,         branch meters, sub-meters, etc.).     -   Per sensitivity (e.g., based on the “initial impact” ranking,         infer coherent groups using clustering techniques in one         possible implementation).

At block 5910, dynamic tolerance curves associated with each metering device (and metering point) are aggregated, for example, based on the rankings and groups defined at block 5905. For example, at block 5905, each metering device may be grouped based three levels of criticality, e.g., high, medium, and low, as defined below.

-   -   High: IEDs that monitor process steps where voltage sags may         disrupt key process steps, generating long interruptions of         process and expensive waste of raw or semi-transformed material.     -   Medium: IEDs that monitor process steps where the voltage sags         result in a cost of non-quality production, but no major         disruption, even during interruptions.     -   Low: IEDs that monitor process steps where interruptions may be         ridden through without any identified or measurable impact.

A system user may want to have two (or more) different dynamic tolerance curves to identify where SagFighers® or other mitigative solutions should be considered, and where any changes in sensitivity to voltage sags needs to be or should be monitored and evaluated. Assuming the three levels of criticality are defined as set forth above, first dynamic tolerance curves may be generated for each IED within the group of “high” meters, e.g., using the “initial impact” to plot the curves of each meter. From there, an aggregate dynamic tolerance curve (i.e., a first aggregate dynamic tolerance curve) may be generated with (or containing) all the values of initial impacts (taking into account the magnitude and duration). Additionally, a second aggregated dynamic tolerance curve tracing the “voltage sag evolution” of each meter over a given relevant time period (e.g., monthly or weekly) of the IED's voltage sags may be generated. As one example, voltage sags may be indicated or plotted in a first color (e.g., red) in the tolerance curve if an IED is identified as becoming more sensitive (=new initial impact threshold for that IED), and in a second color (e.g., light grey) in the tolerance curve if all the voltage sags of a meter fall within a known or predetermined sensitivity.

In some embodiments, aggregate dynamic tolerance curve models may be generated and graphs (or curves) may be generated in response to the models, for example, for the different defined sub-groups of meters or all meters to illustrate the different thresholds and zones in the dynamic tolerance curves.

At block 5915, the aggregated dynamic tolerance curves may be analyzed, for example, for different sub-groups of meters or all meters. For example, for each group normal dynamic tolerance curves may be defined, and abnormalities may be highlighted (such as abnormally high or low sensitivities meters/curves). For a group of IEDs monitoring different load types, for example, a user may want to see which IEDs monitoring motors (i.e., one example type of loads) are more sensitive to voltage sags than others. The user may thus ask for an analysis to provide a list of the IEDs that are significantly more sensitive to voltage sags (from the other IEDs). This requires an analysis which can be based on several steps. For example, cluster groups of meters or identify outliers or extreme outliers based on median values+interquartile range (IQR)*1.5 or IQR*3. This then can be done for “initial impact”, “final impact” and/or for any given value (such as 25% of loads lost).

Additionally, link analysis, time analysis, location analysis, flow analysis, spread analysis, etc. may be conducted to identify possible drivers and pre-occurrences or co-occurrences (switching of loads or BMS or SCADA control actions), recurring patterns (day of week, daily hourly patterns), possible sources (location in hierarchy), etc.

After block 5915, the method may end in some embodiments. In other embodiments, the method may include one or more additional steps, as will be appreciated by one of ordinary skill in the art.

Referring to FIGS. 60-71, various systems and methods for analyzing and optimizing dynamic tolerance curves in accordance with embodiments of this disclosure are described. It is understood that these systems and methods may be implemented, for example, on a processor of at least one metering device (e.g., 121, shown in FIG. 1A) and/or remote from the at least one metering device in at least one of: a cloud-based system, on-site software, a gateway, or another head-end system.

As discussed above, a dynamic tolerance curve may be used to characterize a response characteristic of an electrical system at a respective metering point of a plurality of metering points in the electrical system. As also discussed above, the dynamic tolerance curve may be generated or derived from energy-related signals captured by at least one metering device (e.g., an IED) located or installed at (or proximate to) the respective metering point.

The at least metering device may be configured to monitor one or more loads (e.g., 111, shown in FIG. 1A) in a system (i.e., an electrical system). In embodiments in which the at least one metering device includes a plurality of metering devices, for example, it is possible for at least one of the metering devices to monitor a different number and type of load(s) than at least one other one of the metering devices. For example, depending on the device and/or system configuration(s), some metering devices may monitor only one load (e.g., a critical motor), other metering devices may monitor several loads of the same type (e.g., several identical motors), and other metering devices may monitor a mixture of different types of loads (e.g., motors, painting equipment, and lighting fixtures). The loads and metering devices may be installed in one or more buildings or other physical locations or they may be installed on one or more processes and/or loads within a building or buildings. The buildings may correspond, for example, to commercial, industrial or institutional buildings.

In one example system, a main meter (and other meters) in a building associated with the system may have waveform capture features, such as those described in connection with figures above, for example, to identify load losses and enable load loss calculations (e.g., criticality score and load loss severity). The main meter (and other meters in the building) may have an associated sensitivity threshold for triggering action (e.g., triggering an alarm) on load losses, for example, for at least the reasons discussed in connection with figures above. For example, 30% of load losses may be deemed acceptable for the main meter in some embodiments, while other meters may have a threshold at 10% acceptability. Acceptability thresholds for each metering point or location may be defined or inferred from tags (e.g., system or user tags) and/or from calculations (e.g., the Initial Impact value), and, in some embodiments, be based on the process or processes associated with the metering point or location.

In one example system, the main meter may be responsible for monitoring different processes (and their associated loads). For example, the main meter may be responsible for monitoring: a motor for a conveyer belt associated with a device assembly process, a painting process where heat is used to cook or “bake” paint on a device, and a lighting system of a packaging and storage process. In response to an electrical perturbation (e.g., a voltage sag), the paint process may drop out first at 80% magnitude, the conveyer belt may drop out at 65%, and the lighting system at 60%, for example. In accordance with embodiments of this disclosure, this drop out (or load loss) may be reflected in at least one dynamic tolerance curve, for example, the dynamic tolerance curve shown in FIG. 60 for the paint process, the dynamic tolerance curve shown in FIG. 61 for the conveyer belt, and the dynamic tolerance curve shown in FIG. 62 for the lighting system. This, however, assumes that the loads (and processes) may be isolated from each other (e.g., by having each a dedicated IED measuring the load) in the energy-related signals captured and used to generate the dynamic tolerance curves. This assumes that the system or a user operating or managing the system knows which loads (and processes) are monitored by each IED. For example, the system may be aware of which loads (and processes) are monitored by each meter as it may be linked to SCADA system data and triggers. As is known, this is not always the case.

In embodiments in which the system or the user operating or managing the system does not know which loads (and processes) are monitored by the main meter, a dynamic tolerance curve similar to that shown in FIG. 64 may initially be generated in some embodiments. As illustrated by comparing FIG. 64 with the underlying curves (the same as in each of the FIGS. 60 to 62) added and made visible in FIG. 63, it may be difficult for a user to discern points associated with impacting and non-impacting events for each of the loads (and processes) monitored by the main meter (here, labeled “paint”, “belt” and “light”). For at least these reasons, and reasons described further below, it is an object of the present invention to analyze and optimize dynamic tolerance curves and its related data (e.g., the energy-related signals captured and their derived and/or inferred values such as Load Loss) in accordance with embodiments of this disclosure, for example, to make it easier to discern points associated with impacting and non-impacting events for loads (and processes) monitored by a metering device, such as the above-discussed main meter, for example, in embodiments in which the metering device is responsible for monitoring a plurality of loads (and processes). To this effect, the systems and methods disclosed in the present application are intended to supplement those described in co-pending U.S. patent application Ser. No. 16/137,603, entitled “Dynamic Tolerance Curves for Power Monitoring Systems”, Ser. No. 16/233,214, entitled “Systems and Methods for Managing Power Quality Events in an Electrical System”, Ser. No. 16/233,220, entitled “Systems and Methods for Characterizing Power Quality Events in an Electrical System”, Ser. No. 16/233,231, entitled “Systems and Methods for Managing Voltage Event Alarms in an Electrical System”, Ser. No. 16/233,241, entitled “Supplemental Techniques for Characterizing Power Quality Events in an Electrical System”, and Ser. No. 16/252,112, entitled “Systems and Methods for Analyzing Power Quality Events in an Electrical System”, which applications are assigned to the same assignee as the present disclosure, and are incorporated by reference in their entirety herein. Additionally, the systems and methods disclosed in the present application are intended to supplement those described in PCT Application no. PCT/US19/40536, entitled “Systems and Methods for Analyzing Effects of Electrical Perturbations on Equipment in an Electrical System”, which application is also assigned to the same assignee as the present disclosure, and is incorporated by reference in its entirety herein.

As discussed above, metering devices (and the loads monitored by the metering devices) may have different sensitivities thresholds to electrical perturbations (e.g., voltage sags). Accordingly, the load losses derived from the measurements (i.e., the energy-relate signals) of the metering devices may vary depending on what load(s) is/are running or not running at the moment(s) in time where the electrical perturbations occur. Referring again the above-discussed example in which only one main meter at the building level has the necessary power quality capabilities (e.g., enabling load loss measurements) may be responsible for monitoring a conveyer belt motor, a painting process and a lighting system. If the paint process is the most sensitive part to voltage sags (e.g., at 80% magnitude), and this process runs mainly in the mornings, then if a voltage sag occurs in the morning, more load loss may be measured if the voltage sag stays within a range where the paint process drops out (e.g., <80%), but no other process/load is affected yet (e.g., >65%). However, if the same voltage sag of let's say 70% magnitude occurs in the afternoons, it would be normal that no load loss will be measured because the most sensitive equipment is not running (e.g., as only the paint process drops off above a magnitude of 65% where then the belt process would start being affected).

It has been observed the higher up in the hierarchy of an electrical system a metering device is placed with no more discrimination (i.e., no more sub-meters with waveform capture capabilities/load loss algorithms embedded), the higher the probability of a “fuzzy” data situation similar to the situation described in the paragraph directly above. This may result in a dynamic tolerance curve similar to the dynamic tolerance curve shown in FIG. 64 initially being generated in response to metering device measurements, which dynamic tolerance curve may be difficult for a system user or operator to discern, as noted above.

In accordance with embodiments of this disclosure, the systems and methods disclosed herein provide techniques for analyzing a dynamic tolerance curve generated from metering device measurements to identify distinct groups or clusters of points (e.g., points associated with impacting and non-impacting events for each load) in the dynamic tolerance curve and optimize the dynamic tolerance curve to more clearly indicate load losses.

Referring now to FIG. 65, a graphical representation of the metering devices (e.g., IEDs) capable of measuring the load loss discussed above and below is shown. Referring also to FIG. 65A, it is illustrated that the metering devices may be arranged in a hierarchical configuration (as also shown and described above in connection with various other figures). As also illustrated in FIG. 65A and various other figures, a metering device may be configured to monitor one or more other metering devices (or sub-meters) in an electrical system, for example, as illustrated by metering devices 6505, 6510, 6515. A metering device may also be configured to monitor one or more protection devices or loads with no or limited measurement capabilities (e.g., limited to energy kWh readings), as illustrated by metering device 6520 and protection device 6525. In accordance with various embodiments of this disclosure, one or more metering devices in an electrical system (such as those represented by FIGS. 65 and 65A) may take the form a virtual metering device, which type of device is described earlier in this application.

Referring also to FIG. 65B, it is understood that an electrical system may include one or more zones, with each of the zones capable of including a plurality of loads, for example, as illustrated by zone 6530 in FIG. 65B. As discussed above, it is possible for the loads to be of a same type (e.g., several identical motors), or of a plurality of different types (e.g., motors, painting equipment, and lighting fixtures). As also discussed above, in embodiments in which the system or the user operating or managing the system does not know which types of loads (and processes) are monitored by a metering device, a dynamic tolerance curve similar to that shown in FIG. 64 may initially be generated, for example, based on measurement data collected by the metering device. This may be due, for example, to the loads having different sensitivities to electrical perturbations (e.g., voltage sags).

In the example electrical system shown in FIGS. 65B and 65C, metering device 6520 may capture energy-related signals associated with each of the protection devices/loads in the zone 6530. From these energy-related signals, and in some cases logged data (e.g., from the protection devices/loads), three different sensitivities (or sensitivity levels) may be inferred. For example, for a first group of protection devices/loads (as indicated by 6535 in FIG. 65C) in the zone 6530, it may be inferred that the group has a sensitivity of about 60% of the voltage sag magnitude. Additionally, for a second group of protection devices/loads (as indicated by 6540 in FIG. 65C) in the zone 6530, it may be inferred that the group has a sensitivity of about 65% of the voltage sag magnitude. Further, for a third group of protection devices/loads (as indicated by 6545 in FIG. 65C) in the zone 6530, it may be inferred that the group has a sensitivity of about 80% of the voltage sag magnitude. Techniques for identifying/inferring groups are described further below.

In accordance with some embodiments of this disclosure, a user of the system may manually look at the values (e.g., points) displayed in a dynamic tolerance curve and identify/deduce the groups which seem obvious or the more probable to the user. In the above example, the user may identify groups relevant to the user, for example, either because the user knows the time (process) described, or because the user is capable of identifying the similar sensitivities (e.g., by looking at the dynamic tolerance curve). One example advantage of this approach is the user “owns” the groups the user made. Additionally, the user knows how the groups were chosen. In some instances, the user has in depth knowledge of the loads and processes.

The above-discussed approach may work well in instances when you have a few occurrences of sags. However, as soon as you have a large number of points (e.g., due to the number of sags or to the longer time periods being analyzed to try to find trends or groups), some points may be overlaid above others (e.g., as shown in FIGS. 66 and 67), and the human brain will be unable to efficiently visually disaggregate and/or analyze the results (this is a well-known and documented issue in the field of graphical representations for visualization and visual analysis) and to discern the different sensitivities to voltage sags. Another limitation is due to the relative importance of load loss beyond the initial impact (what percentage of load loss at initial point vs. the curve of load loss as the voltage continues to drop), adding a second dimension to be analyzed and which is not taken into the account in this representation (see, for example, previously mentioned 3D plots in this disclosure which would add another dimension not readable for the human eye). The human brain has difficulty performing these calculations while looking at the plots.

For at least the reasons discussed above, it may be desirable to at least partially automate the grouping process. For example, the grouping process may be automated through a rules-based process using automatic bins. For example, a user may define a certain number of rules which the system may automate. The user may, for example, create a plurality of bins, with each (or most) bins associated with a step of range of 10% of magnitude, starting at 90% till 50% (e.g, 90-81, 80-71, 70-61, 61-51). Additionally, all events below 50% may be grouped into one final bin, for example. The system/user may then use each bin to create a specific dynamic tolerance curve. The system/user then relies on the user's knowledge of the system and of which process was running at each voltage sag to conduct an analysis of each dynamic tolerance curve.

The more data, the more a statistical and/or machine learning approach or tool may be valid or become an essential part of the solution to identifying or inferring groups. The massive data influx may originate from many different sources, for example, many voltage sags, many different loads under the lowest IED (or metering device) which has power quality capabilities (e.g., Waveform capture features), many different non-power quality measurement points providing other useful measurements (e.g., kWh meters or protection devices) at the lower levels of the hierarchy, and/or many different sensitivity levels. In using the statistical and/or machine learning approach or tool, it may be possible to detect loads that are more/less sensitive to load losses, and thus determine groups of running loads (mix of running processes) that need to be distinguished. A hybrid approach may be suitable in some embodiments. For example, the user (e.g., an end-user) may enter “some” points and the system may interpolate/extrapolate from that data using the statistical and/or machine learning approach or tool.

In accordance with some embodiments of this disclosure, a dynamic tolerance curve may be generated for each of the groups (or select groups), for example, groups of loads. Referring again to the system including a metering device configured to monitor a conveyer belt motor, a painting process, and a lighting system, for example, a dynamic tolerance curve may be generated for each group of loads (distinctive groups of sensitivity of mixes of running processes). Example of “Mornings” (paint process active), afternoons #1 (no paint process active, motor for the belt and lighting are both active), and afternoons #2 (when only lighting is active).

In accordance with some embodiments of this disclosure, statistical and machine learning methods/approaches may be used to discriminate between these groups. For example, these groups can be inferred automatically using state of the art techniques in the statistical and artificial intelligence fields, these techniques referring to tools and processes falling under the very broad field of “unsupervised machine learning”, for example, clustering using Kmeans, DBSCAN, trees derived methods, dimension reduction such as PCA, etc. As is known, these tools and methods aim at deducing from the data relevant groups (aka “clusters”) based on statistical optimization of the number of clusters, the distance between the clusters, and the internal coherence of the clusters. This by nature fits the “automatic inference” of this part of the method.

The system (i.e., a system that consumes, analyzes, processes, and/or presents the data, for example, a processor of at least one IED, a cloud-based system, on-site software, a gateway, or another head-end system) may deduce clusters from magnitude analysis of similar sensitivities resulting in finding three thresholds of 80%, 65% and 60% magnitude. However, there could be different combinations of loads within each group running at a given time, even for the 80% or the 65% sensitivity group. These groups can thus be sub-clustered as a result of applying clustering techniques using as a reference the load loss percentage value as the key. It is also possible to add another step wise clustering or to add another characteristic such as clustering to the voltage sag events, by separating which loads/processes are of high criticality, vs. the less critical loads/processes.

As briefly discussed above, the system also allows for hybrid methods and processes. For example, an end-user may enter “some” known groups which seem logical to the user and will help the user in analyzing or operating the results (e.g., relying on his knowledge of the process the user may choose to group certain IEDs as being process wise linked or related, and create other groups with IEDs which are completely separated process wise). The system may then classify the other IED into these groups manually created by the user, and the system may infer any other relevant group or series of groups from the data. One way of identifying such new groups is to use the existing groups as references to calculate what characteristics or values distinguish them from each other. The system may then infer from the data additional groups which may be “missing” such as when some other discriminant characteristics are not taken into account by the user groupings.

Statistically speaking, this is then a mix of methods we could refer to as a semi-supervised clustering. This may be defined as any method where an input is used as starting groups or within an iteration of the process (e.g., referred to as a “manual clusters”) and where these groupings are either used as an “absolute grouping” which may not be changed by the system (e.g., when a process is linked to a defined list of IEDs), or as an “indicative grouping” which may be fine-tuned by the system (e.g., by moving some IEDs from one group to another which is statistically better suited based on the characteristics and values combinations typical and discriminant for each cluster).

As described above, the system may then test the groupings from a statistical point of view providing a fitness of manual clustering score (e.g., a score based on the percentage of the number of IEDs which may be clustered discriminately by using these manual groupings versus the total number of IEDs). The system may then calculate any other discriminant groups based on the characteristics and values which may have been missed by the manual groupings. This may be done in many different ways such as for illustration purposes only provided approaches: The system may perform a clustering of all IEDs without using the manually entered clusters as a first step of the process (this clustering step will later be referred to as “unconstrained clustering”). Using the calculated clusters, it then deduces which characteristics and values are discriminant for each cluster. The system then compares the manually entered clusters with these statistically inferred clusters. It identifies which characteristics and values are discriminant for the manual clusters as well. Any overlapping clusters will then go through a resolution process which is strongly influenced by the “absolute grouping” or “indicative grouping” settings.

In the case of “absolute grouping” setting, only discriminant other new clusters will be added (no modification to manual groupings will be performed). Using these as inputs, the non-classified IED by the user will then be classified into the most relevant clusters available (mix of the manual defined clusters and the system added new clusters). In the case of the “indicative grouping”, the system will create the same new clusters as above. But the system will also allow for a modification of the manual groups. This process may be done by applying different methods such as for not limitative illustration purposes following example of method: The system may use the outputs of the “unconstrained clustering” to deduce which of its clusters have overlapping characteristics, values and IEDs, with the manual groupings characteristics, values and IEDs.

Once any overlap is identified, the system will either enrich the manual clusters (e.g., add new characteristics or values range as being relevant to a manual group such as when splitting a “unconstrained cluster” into two or more manual groupings if they are in fact sub-divisions of the “unconstrained clustering”) thereby classifying the IEDs of the “unconstrained cluster” into the relevant (sub-clusters) manual groupings. Or the system may regroup some of the manual groupings into one or more “unconstrained clusters”, keeping only the manual groupings which are distinct from the “unconstrained clusters” (even if this implies having less statistically relevant groups, as these may make good sense from a process perspective).

The choice may be a default setting provided by the user and/or by the system, or may be the result of a user or other system interaction (e.g., having a graphical interface with user, or relying on scenario testing with a manufacturing SCADA system to see best fit to processes). Another way of achieving a similar result may be to used semi supervised learning techniques by “teaching the system” the manual groupings as inputs. This refers to “state of the art” in the field of Artificial intelligence. This example of combining into hybrid modes a process steps or model with any other process step or any other process described here-within application shows that any of the elements may be combined with another element to create richer and more complex models or processes.

It is understood that there are many other rules based possible approaches. For example, using the manufacturing SCADA system information as well as the Building Management System information, we can deduce for each Load loss case, which processes were active in each case.

It may be important here to highlight additional drivers which motivate the use of methods and processes to automatically infer from the data and meta data relevant groupings. These drivers may also apply for the other steps, methods and processes describe in this application (e.g., on the automatically inferred best fit curves).

As briefly discussed above, the more data, the more a statistical approach may be valid or become an essential part of the solution to solve the problem. In additional to the sources of large data influxes discussed above, large data influxes may originate from data sets coming from other systems (e.g., running processes information coming from a Manufacturing SCADA system). Additionally, large data influxes may originate from many variations in sensitivities of control devices (e.g., electronic components' sensitivities inside the control of the equipment, such as on contactors). Some may ride-through the same magnitude, while others may trip. To illustrate this principle of “Non-linearities in the ride-through characteristics of Components, Loads and Processes”, contactors may be a good example among others.

The effects of voltage perturbations on some loads (e.g., AC motor contactors shown in FIG. 68) will be influenced by a number of voltage sag characteristics including magnitude, duration, phase angle of the voltage sag's onset, line impedance, and the size of the contactor's coil. As is shown in the FIG. 68, certain “non-linearities” exist in the relationship between a voltage sag event's magnitude and duration, which is illustrated by the ride-through behavior of three exemplary Size 0, Size 1 and Size 2 AC motor contactors. In all three cases, the relationship between a voltage sag's magnitude and the duration of the respective contactor's ride-through is somewhat unexpected. For example, a voltage sag event to approximately 41% of the nominal voltage will result in the Size 0 AC motor contactor to dropping out within 16 milliseconds. Similarly, a voltage sag to approximately 49% of the nominal voltage will result in the Size 1 AC motor contactor dropping out within 27 milliseconds. Finally, a voltage sag to approximately 38% of the nominal voltage will result in the Size 2 AC motor contactor dropping out within 52 milliseconds.

What seems to be incongruous is that as the voltage sag depth increases (approaches zero volts), the ride-through characteristics of all three AC motor contactors actually improves. For example, the ride-through duration for the Size 0, Size 1 and Size 2 AC motor contactors is 57 milliseconds, 63 milliseconds, and 109 milliseconds, respectfully. In short, some components/loads/processes may not react as expected when experienced a voltage perturbation.

In accordance with some embodiments of this disclosure, the systems and methods disclosed herein may optimize boundaries or threshold lines in a dynamic tolerance curve. As discussed above, the boundaries or threshold lines are used to indicate load loss(es) in the electrical system. In some embodiments, the boundary or threshold line optimization process is a fully automated or hybrid approach.

In one example implementation, the boundary or threshold line optimization process includes automatic identification and removal of statistical outliers. Sometimes, only few points will create a visual perception of “fuzzy” or unclear boundaries. Additionally, sometimes the other relevant dimensions may not be visible (e.g., if we only show the magnitude vs. duration, then we may miss the percentage of load loss while the user or system may consider or know at which threshold of Load loss the impact is considered “negligible” vs. “critically impacting” for the process). In this case, the system may choose to not use these points for defining I “drawing” the boundaries line (as the user may do in a manual process). The system may then produce a specific drawing to show how the boundary line was calculated by removing or specifically tagging outliers), for example, as shown in FIG. 69.

Referring to FIG. 69, the points highlighted in this plot may be considered “outliers” or “less reliable” from a statistical approach. In this case, the system will draw lines such as illustrated. Additionally, the system may warn a user about these extreme new points (e.g., such as in red color or a particular type of shading in a graph presented within a report, providing all additional information such as “these 3 points have only 5% load loss while all the other points exceed 20% of load loss, a threshold which is set by users as below which the impact is considered “negligible” for the process and running operations).

In some embodiments, the boundaries or threshold lines are automatic statistical or machine learning deduced boundaries or threshold lines. As understood, there are many different methods, algorithms which may help discriminate between overlapping data points groups. This is considered “state of the art” in the fields of Statistics and Artificial intelligence (which includes all the Machine Learning algorithms). The aim of all these methods is to discriminate between two groups (the impacting vs. the non-impacting groups) in this present application. A non-limitative list examples of methods include, for example, Logistic regression, linear discriminant analysis, quadratic discriminant analysis, KNN (k-nearest neighbor) classifier, splines, local regression, general additive models, generalized additive models, tree based methods and derived bagging, random forest, boosting, support vector machine, tensor and other AI, deep learning linked methods.

In another aspect of this disclosure, we claim to innovate in the fact that we may add a constraint to any of these pure statistical approaches. In accordance with some embodiments of this disclosure, the boundary corresponds to an optimal vertical moving “line” which only has one X value for any given Y value and the X-Y pairs need to create a continues line. This principle may be illustrated, for example, with a KNN (k-nearest neighbor) example. Referring to FIG. 70, an example graphical KNN representation is shown. In accordance with some embodiments of this disclosure, after applying the above-discussed constraint, the line would be corrected as illustrated in dashed line (representing a portion of the dynamic tolerance curve).

As we may be applying the different algorithms by adding a specific constraint, we may then need to add an adjusting or tuning step after performing the calculation of any model to optimize the parameters (e.g., in the case of the KNN the system would optimize the “misclassified points” by varying the k value, as is state of the art in machine learning). Our constraint adds a “mechanical” constraint (this is an additional data dimension) which was not yet included in the original statistical calculations. The implementation will always be specific to the method chosen. In the case of the KNN taken as one example amongst many, the system will optimize the choice by changing the K value, for example, from K=15, as shown in FIG. 71, to K=150, as shown in FIG. 72. As illustrated in FIG. 72, with K=150, the KNN becomes a simple regression line. The optimization may be performed by many different calculations (e.g., by calculating a score such as number of points misclassified by applying the constraint for a given K divided by the number of points misclassified by applying the constraint for a given K−1. The optimal could then be the k which minimizes this score).

This adaptation method also includes the possibility of automating the tests (e.g., statistical assumptions tests) to remove non-applicable methods (e.g., if data is not following an expected Gaussian distribution), or to test if application of data transformation (e.g., among many others log(Y) or sqrt(Y) which results in shrinks the larger responses) enables to meet a Gaussian assumption. Cross validation/bootstrap or tensor layer optimization methods may also be used to choose best machine learning calculation method (e.g., what is best model) as well as to detect any change over time in the models or parameters of the models (e.g., would any new data period change the optimal number of the degree of freedom if using a logistic regression), or tensors layers and optimal number of layers.

As some methods will result in some more adaptive curve (lines will be smoothers, breakpoints less clearly defined), the system will need to be set or to automatically define what is more important: Precision or readability (e.g., have few clear breakpoints to configure for alarms). The user or the system (if user profiles are defined as “non-specialist profiles”, or if a manufacturing SCADA system control is being tuned) can then define a priority either towards easier interpretation or towards a higher precision. The system may apply different possible scenarios. As two examples among many others, the system may remove the more convoluted curves (e.g., by keeping only linear methods), or the system may reduce the perceived complexity by reducing the number of possible breakpoints.

In some embodiments, multiple approaches may be combined. However, more often than not (in some instances), the results and/or situations may become too complex, such as when the number of possible or measured combinations of processes becomes massive and does not provide a list of few simple dynamic tolerance curve (e.g., if a building has three different “Paint” lines, five different motors for the “Conveyor belts”, and two locations for “Lighting” packaging and three “Metal sheet press” machines with each two motors).

We can for example combine the approaches by applying first the grouping into more coherent groups (e.g., using the automated statistical approach described previously), then deducing the best fit boundary lines (e.g., applying the “constrained KNN” method described previously). Finally, the user may be, in our present example, presented with a graphical user interface showing a dynamic tolerance curve where the user can move some points manually. Then the system iterates through the above steps until the user is satisfied by the result (e.g., by validating a proposed series of dynamic tolerance curve(s), or by not changing anything to the any of the proposed dynamic tolerance curve(s)).

It is also possible to create more interactive hybrid derived combinations such as to enable users to select additional inputs for the system to use. For example, there could be a hybrid approach where the end-user enters “some” points in the dynamic tolerance curve boundary line and the system interpolates/extrapolates/optimizes the rest of the boundary line from that data. In another example, the user could visualize the impact of changing a value such as the K value, creating more or less complex lines.

In accordance with some embodiments of this disclosure, it may become essential (helpful) over time to optimize the overall results for best usage. It is understood that this “best usage” is a very large encompassing concept which includes many different criteria such as three non-limitative examples: for fast readability, for coherence over time so as to enable period to period comparison, for control of SCADA. By evaluating the data statistically over time, for example, the data should converge on an acceptable solution. The system thus will proceed by iterating through the stages of testing models and testing the coherence of the produced dynamic tolerance curves and iterate further until optimal solution according to the “best usage” criteria is found. This process may include each step (grouping calculations, boundary line calculations, any additional steps or mixture of steps). But it may also be very relevant and used for the global final result.

In accordance with some embodiments of this disclosure, it is also possible to add optional methods and/or steps to provide users with more “stable” dynamic tolerance curves over time. For example, a stability of used and applied models may be created by adding a penalty factor over time (e.g., multiplying the score by the number of models used) so that any model deviating from previous relevant model(s) is strongly penalized. Additionally, a stability of groups may be created by adding a penalty score for groups being split further (e.g., multiply the score by the number of clusters). If the new penalized score is below the previous relevant score, the benefits of the new calculations exceed the added complexity. If not, it may be decided to stay with previous methods and previous clusters.

It is understood that the above discussed systems and methods may be combined with one or more of the systems and methods described throughout the disclosure. Additionally, it is understood that various features and of the above discussed systems and methods may be applied to other concepts in this application. For example, the above discussed systems and methods may be also be used to distinguish data in aggregated dynamic tolerance curves. It is understood that dynamic tolerance curves may generated for a particular period of time (e.g., a day). It is also understood that dynamic tolerance curves may indicate, or be used in connection with, reports and alarms. Additionally, it is understood that dynamic tolerance curves may indicate, for example, % of load loss Vs. economic loss graphs with relative size of sags, voltage sags vs. economic, interpretation for financial users & ROI (e.g., “how much do I gain back from the economic side”), load loss vs. CO₂ (recovering energy=costs), recovery time vs. load loss, and any other number of other items that are apparent to one of ordinary skill in the art.

As illustrated above, and as will be appreciated by one of ordinary skill in the art, embodiments of the disclosure provide many benefits. Example benefits include latching evaluations to detected power quality events (anomalous electrical conditions) and non-latching evaluations of load starting and restarting (i.e., duty cycles). Example benefits also include identifying potential restart issues, for example, using market segment metadata to determine criticality of issue to customer type (e.g., injection molding systems may have one goal; refrigeration systems may have another), and identifying some potential equipment types that are restarting. Additional benefits include quantifying potential restart issues, for example, by location(s), number of restarts associated with discrete power quality events such as voltage sags and undervoltage conditions and accumulating other restart information associated with specific event characteristics (i.e., magnitude, duration, frequencies present, etc.). Further benefits include reducing equipment stress, extending equipment operational life, reducing maintenance costs, inhibiting the notification of unexpected or excessive equipment restarts (if they are deemed to be acceptable), and recommending mitigative techniques to minimize/eliminate restart issues (if they exist).

Embodiments of this disclosure also provide end-users with a better understanding of how equipment is operating on their electrical system. For example, embodiments of this disclosure provide end-users with detailed information on the impact of electrical perturbations (and anomalous voltage conditions) to their loads. Leveraging data from normal and abnormal voltage conditions as provided by metering instrumentation or other IEDs provides the ability to identify, quantify, and notify end-users of the universal and isolated stresses experienced across their electrical system and/or at discrete loads. When deployed in cloud-based applications, the disclosure may allow statistical analysis, analytics, baselining and/or classifying across comparable market segments and/or equipment, identifying opportunities for new solutions and services, and strengthening equipment, customer, and market awareness.

As also illustrated above, and as will be appreciated by one of ordinary skill in the art, embodiments of the disclosure promote “more and better” metering within facilities. For example, the more IEDs installed in an energy consumer's electrical system, the more beneficial these embodiments may be for the energy consumer. As will also be appreciated by one of ordinary skill in the art, there are significant opportunities for voltage event mitigation products. Further, it will be appreciated by one of ordinary skill in the art that it is important to identify and promote opportunities that would have typically been overlooked, misunderstood, or simply ignored by energy consumers. The ability to quantify voltage events creates a justifiable sense of urgency for the energy consumer to resolve these issues. The various embodiments described in this disclosure should allow services-based organizations to more readily identify opportunities and be retained for designing and installing the most economical solution. By leveraging products to identify opportunities for improving voltage event mitigation and reduced recovery time, for example, energy consumers may improve their operational availability and increase their profitability.

The embodiments described in this disclosure may also create many opportunities for cloud-based services. While the prospect of using on-site software to evaluate, quantify, and mitigate voltage events may be more ideal in some embodiments, direct (or substantially direct) participation/interaction with energy consumers may tend to promote many more services and products sales opportunities. By evaluating the voltage event data in the cloud, active engagement in a timelier manner with relevant information and practical solutions may yield further possibilities.

As illustrated above, voltage sags/dips have a significant impact on industrial equipment, processes, products, and ultimately a customer's bottom-line. In embodiments, voltage sags/dips are the biggest (or close to the biggest) source of power quality issues, and can originate both inside and outside an energy consumer's facility. Using dynamic voltage tolerance curves and the other embodiments described herein will provide the ability to localize, quantify, and rectify the impact of voltage sags/dips and shorten event recovery time. Moreover, dynamic voltage tolerance curves provide the ability to target, design and validate custom mitigative solutions and services, which helps the energy consumer reduce interruptions to their operations, maximize their system performance and availability, increase their equipment life, and reduce their total operating costs. In short, the embodiments disclosed in this application may be incorporated in meters, gateways, on-site software such as PME, and cloud-based offers such as Power Advisor by Schneider Electric.

As described above and as will be appreciated by those of ordinary skill in the art, embodiments of the disclosure herein may be configured as a system, method, or combination thereof. Accordingly, embodiments of the present disclosure may be comprised of various means including hardware, software, firmware or any combination thereof.

It is to be appreciated that the concepts, systems, circuits and techniques sought to be protected herein are not limited to use in the example applications described herein (e.g., power monitoring system applications) but rather, may be useful in substantially any application where it is desired to manage power quality events in an electrical system. While particular embodiments and applications of the present disclosure have been illustrated and described, it is to be understood that embodiments of the disclosure not limited to the precise construction and compositions disclosed herein and that various modifications, changes, and variations can be apparent from the foregoing descriptions without departing from the spirit and scope of the disclosure as defined in the appended claims.

Having described preferred embodiments, which serve to illustrate various concepts, structures and techniques that are the subject of this patent, it will now become apparent to those of ordinary skill in the art that other embodiments incorporating these concepts, structures and techniques may be used. Additionally, elements of different embodiments described herein may be combined to form other embodiments not specifically set forth above.

Accordingly, it is submitted that that scope of the patent should not be limited to the described embodiments but rather should be limited only by the spirit and scope of the following claims. 

What is claimed is:
 1. A method for analyzing power quality events in an electrical system, comprising: processing electrical measurement data from or derived from energy-related signals captured by at least one metering device in the electrical system to generate at least one dynamic tolerance curve, wherein each dynamic tolerance curve of the at least one dynamic tolerance curve characterizes a response characteristic of the electrical system at a respective metering point in the electrical system; analyzing the at least one dynamic tolerance curve to identify special cases which require further evaluation(s)/clarification to be discernable and/or actionable by the system that consumes, analyzes, processes, and/or presents the data or a user; and regenerating the at least one dynamic tolerance curve, updating the at least one dynamic tolerance curve, and/or generating new or additional dynamic tolerance curves to provide the further clarification, wherein an indication of at least one identified power quality event is provided on the at least one dynamic tolerance curve, wherein the at least one identified power quality event is characterized as either an impactful power quality event or a non-impactful power quality event based on a determined impact of the at least one power quality event.
 2. The method of claim 1, wherein providing the further clarification includes providing additional information on the regenerated or updated at least one dynamic tolerance curve, and/or on the new or additionally generated dynamic tolerance curves, to make the regenerated and/or updated at least one dynamic tolerance curve, and/or the the new or additionally generated dynamic tolerance curves, discernable and/or actionable by the system.
 3. The method of claim 1, wherein providing the further clarification includes using at least one of statistical, machine learning, rules based and other computational method or methods, process or processes, or tool or tools to provide the further clarification.
 4. The method of claim 1, further comprising: adjusting the regenerated or updated at least one dynamic tolerance curve, and/or the new or additionally generated dynamic tolerance curves, to optimize the regenerated or updated at least one dynamic tolerance curve and/or the new or additionally generated dynamic tolerance curves.
 5. The method of claim 1, wherein the further clarification is provided, at least in part, in response to one or more user inputs or interactions.
 6. The method of claim 1, wherein providing the indication of the at least one power quality event includes quantifying the impact of the at least one power quality event one or more loads in the electrical system.
 7. The method of claim 3, wherein using the at least one of statistical, machine learning, rules based and other computational method or methods, process or processes, or tool or tools to provide the further clarification, includes: determining a best method, technique, process or tool, or a best combination of methods, techniques, processes or tools to provide the further clarification; and using and adjusting the determined best method, process or tool, or best combination of methods, processes or tools, to provide the further clarification.
 8. The method of claim 6, wherein the impact is quantified based on relative (%) of load impact, absolute load impact, etc.
 9. The method of claim 7, wherein the best method is determined based on at least one of: the application, location within the system, type of data being analyzed, type of event being analyzed, impact of event, scope of event, changes over time periods, determined patterns, etc.
 10. The method of claim 7, wherein using the determined best method, process or tool, or best combination of methods, processes or tools, to provide the further clarification, includes: grouping points in the regenerated or updated at least one dynamic tolerance curve, and/or in the new or additionally generated dynamic tolerance curves, based on one or more load loss characteristics.
 11. The method of claim 7, wherein using the determined best method, process or tool, or best combination of methods, processes or tools, to provide the further clarification, includes: optimizing boundaries or threshold lines in the regenerated or updated at least one dynamic tolerance curve, and/or in the new or additionally generated dynamic tolerance curves.
 12. The method of claim 10, wherein the load loss characteristics include load sensitivity to electrical perturbations.
 13. The method of claim 11, wherein optimizing the boundaries or threshold lines includes identifying or removing statistical outliers.
 14. The method of claim 11, wherein the boundaries or threshold lines are automatic statistical or machine learning deduced boundaries or threshold lines.
 15. The method of claim 11, wherein the boundaries or threshold lines are used to indicate one or more potential load loss(es) in the electrical system.
 16. The method of claim 12, wherein the load sensitivity is based on at least one of load/component/system/process type(s), load/component/system/process location(s), event magnitude(s), and event duration(s) in the electrical system.
 17. The method of claim 14, wherein the boundaries or threshold lines automatically calculated by machine learning or statistical methods or tools correspond to optimal vertical moving boundaries or lines which have only one X value for any given Y value and the X-Y pairs create continuous boundaries or lines.
 18. A method for analyzing power quality events in an electrical system, comprising: processing electrical measurement data from or derived from energy-related signals captured by at least one metering device in the electrical system to generate at least one dynamic tolerance curve, wherein each dynamic tolerance curve of the at least one dynamic tolerance curve characterizes a response characteristic of the electrical system at a respective metering point in the electrical system; analyzing the at least one dynamic tolerance curve to identify special cases which require further evaluation(s)/clarification to be discernable and/or actionable by the system that consumes, analyzes, processes, and/or presents the data or a user; regenerating the at least one dynamic tolerance curve, updating the at least one dynamic tolerance curve, and/or generating new or additional dynamic tolerance curves to provide the further clarification; and automatically performing one or more actions affecting at least one component in the electrical system, and/or initiating another process in a manufacturing SCADA or changes to the process(es), in response to a load loss being outside of a range indicated in the regenerated and/or updated at least one dynamic tolerance curve, and/or the new or additionally generated dynamic tolerance curves, wherein an indication of at least one identified power quality event is provided on the at least one dynamic tolerance curve, wherein the at least one identified power quality event is characterized as either an impactful power quality event or a non-impactful power quality event based on a determined impact of the at least one power quality event.
 19. The method of claim 18, wherein the action affecting at least one component of the electrical system is automatically performed by a control system associated with the electrical system.
 20. The method of claim 18, wherein the at least one component includes one or more loads monitored by the at least one metering device. 